#%matplotlib inline
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML
# Set random seed for reproducibility
manualSeed = 999
#manualSeed = random.randint(1, 10000) # use if you want new results
print("Random Seed: ", manualSeed)
random.seed(manualSeed)
torch.manual_seed(manualSeed)
torch.use_deterministic_algorithms(True) # Needed for reproducible results
from pathlib import Path
Random Seed: 999
# Root directory for dataset
dataroot = Path("../data/leedsbutterfly/images")
# Number of workers for dataloader
workers = 2
# Batch size during training
batch_size = 256
# Spatial size of training images. All images will be resized to this
# size using a transformer.
image_size = 64
# Number of channels in the training images. For color images this is 3
nc = 3
# Size of z latent vector (i.e. size of generator input)
nz = 100
# Size of feature maps in generator
ngf = 64
# Size of feature maps in discriminator
ndf = 64
# Number of training epochs
num_epochs = 1000
# Learning rate for optimizers
lr = 0.0002
# Beta1 hyperparameter for Adam optimizers
beta1 = 0.5
# Number of GPUs available. Use 0 for CPU mode.
ngpu = 1
# We can use an image folder dataset the way we have it setup.
# Create the dataset
dataset = dset.ImageFolder(root=dataroot,
transform=transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
# Create the dataloader
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
shuffle=True, num_workers=workers)
# Decide which device we want to run on
device = torch.device("cuda:0" if (torch.cuda.is_available() and ngpu > 0) else "cpu")
# Plot some training images
real_batch = next(iter(dataloader))
plt.figure(figsize=(8,8))
plt.axis("off")
plt.title("Training Images")
plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)))
<matplotlib.image.AxesImage at 0x7f606154ed60>
# custom weights initialization called on ``netG`` and ``netD``
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
# Generator Code
class Generator(nn.Module):
def __init__(self, ngpu):
super(Generator, self).__init__()
self.ngpu = ngpu
self.main = nn.Sequential(
# input is Z, going into a convolution
nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(True),
# state size. ``(ngf*8) x 4 x 4``
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
# state size. ``(ngf*4) x 8 x 8``
nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
# state size. ``(ngf*2) x 16 x 16``
nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
# state size. ``(ngf) x 32 x 32``
nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),
nn.Tanh()
# state size. ``(nc) x 64 x 64``
)
def forward(self, input):
return self.main(input)
# Create the generator
netG = Generator(ngpu).to(device)
# Handle multi-GPU if desired
if (device.type == 'cuda') and (ngpu > 1):
netG = nn.DataParallel(netG, list(range(ngpu)))
# Apply the ``weights_init`` function to randomly initialize all weights
# to ``mean=0``, ``stdev=0.02``.
netG.apply(weights_init)
# Print the model
print(netG)
Generator(
(main): Sequential(
(0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
(6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(inplace=True)
(9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(11): ReLU(inplace=True)
(12): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(13): Tanh()
)
)
class Discriminator(nn.Module):
def __init__(self, ngpu):
super(Discriminator, self).__init__()
self.ngpu = ngpu
self.main = nn.Sequential(
# input is ``(nc) x 64 x 64``
nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
# state size. ``(ndf) x 32 x 32``
nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
# state size. ``(ndf*2) x 16 x 16``
nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
# state size. ``(ndf*4) x 8 x 8``
nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
# state size. ``(ndf*8) x 4 x 4``
nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, input):
return self.main(input)
# Create the Discriminator
netD = Discriminator(ngpu).to(device)
# Handle multi-GPU if desired
if (device.type == 'cuda') and (ngpu > 1):
netD = nn.DataParallel(netD, list(range(ngpu)))
# Apply the ``weights_init`` function to randomly initialize all weights
# like this: ``to mean=0, stdev=0.2``.
netD.apply(weights_init)
# Print the model
print(netD)
Discriminator(
(main): Sequential(
(0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(1): LeakyReLU(negative_slope=0.2, inplace=True)
(2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): LeakyReLU(negative_slope=0.2, inplace=True)
(5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): LeakyReLU(negative_slope=0.2, inplace=True)
(8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(10): LeakyReLU(negative_slope=0.2, inplace=True)
(11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)
(12): Sigmoid()
)
)
# Initialize the ``BCELoss`` function
criterion = nn.BCELoss()
# Create batch of latent vectors that we will use to visualize
# the progression of the generator
fixed_noise = torch.randn(64, nz, 1, 1, device=device)
# Establish convention for real and fake labels during training
real_label = 1.
fake_label = 0.
# Setup Adam optimizers for both G and D
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
# Training Loop
# Lists to keep track of progress
img_list = []
G_losses = []
D_losses = []
iters = 0
print("Starting Training Loop...")
# For each epoch
for epoch in range(num_epochs):
# For each batch in the dataloader
for i, data in enumerate(dataloader, 0):
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
###########################
## Train with all-real batch
netD.zero_grad()
# Format batch
real_cpu = data[0].to(device)
b_size = real_cpu.size(0)
label = torch.full((b_size,), real_label, dtype=torch.float, device=device)
# Forward pass real batch through D
output = netD(real_cpu).view(-1)
# Calculate loss on all-real batch
errD_real = criterion(output, label)
# Calculate gradients for D in backward pass
errD_real.backward()
D_x = output.mean().item()
## Train with all-fake batch
# Generate batch of latent vectors
noise = torch.randn(b_size, nz, 1, 1, device=device)
# Generate fake image batch with G
fake = netG(noise)
label.fill_(fake_label)
# Classify all fake batch with D
output = netD(fake.detach()).view(-1)
# Calculate D's loss on the all-fake batch
errD_fake = criterion(output, label)
# Calculate the gradients for this batch, accumulated (summed) with previous gradients
errD_fake.backward()
D_G_z1 = output.mean().item()
# Compute error of D as sum over the fake and the real batches
errD = errD_real + errD_fake
# Update D
optimizerD.step()
############################
# (2) Update G network: maximize log(D(G(z)))
###########################
netG.zero_grad()
label.fill_(real_label) # fake labels are real for generator cost
# Since we just updated D, perform another forward pass of all-fake batch through D
output = netD(fake).view(-1)
# Calculate G's loss based on this output
errG = criterion(output, label)
# Calculate gradients for G
errG.backward()
D_G_z2 = output.mean().item()
# Update G
optimizerG.step()
# Output training stats
if i % 50 == 0:
print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
% (epoch, num_epochs, i, len(dataloader),
errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))
# Save Losses for plotting later
G_losses.append(errG.item())
D_losses.append(errD.item())
# Check how the generator is doing by saving G's output on fixed_noise
if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):
with torch.no_grad():
fake = netG(fixed_noise).detach().cpu()
img_list.append(vutils.make_grid(fake, padding=2, normalize=True))
iters += 1
Starting Training Loop... [0/1000][0/4] Loss_D: 1.5046 Loss_G: 6.4261 D(x): 0.7725 D(G(z)): 0.6382 / 0.0030 [1/1000][0/4] Loss_D: 0.1413 Loss_G: 7.3645 D(x): 0.9813 D(G(z)): 0.1048 / 0.0010 [2/1000][0/4] Loss_D: 0.1555 Loss_G: 9.3220 D(x): 0.9699 D(G(z)): 0.0988 / 0.0002 [3/1000][0/4] Loss_D: 0.0768 Loss_G: 10.3860 D(x): 0.9923 D(G(z)): 0.0644 / 0.0001 [4/1000][0/4] Loss_D: 0.0289 Loss_G: 7.1842 D(x): 0.9969 D(G(z)): 0.0251 / 0.0010 [5/1000][0/4] Loss_D: 0.0113 Loss_G: 11.4426 D(x): 0.9904 D(G(z)): 0.0000 / 0.0000 [6/1000][0/4] Loss_D: 0.2582 Loss_G: 13.6945 D(x): 0.9807 D(G(z)): 0.1919 / 0.0000 [7/1000][0/4] Loss_D: 0.0041 Loss_G: 9.0673 D(x): 0.9966 D(G(z)): 0.0006 / 0.0002 [8/1000][0/4] Loss_D: 0.0326 Loss_G: 15.9179 D(x): 0.9759 D(G(z)): 0.0000 / 0.0000 [9/1000][0/4] Loss_D: 0.0283 Loss_G: 23.9272 D(x): 0.9816 D(G(z)): 0.0000 / 0.0000 [10/1000][0/4] Loss_D: 4.2341 Loss_G: 24.3291 D(x): 0.1203 D(G(z)): 0.0000 / 0.0000 [11/1000][0/4] Loss_D: 1.3951 Loss_G: 24.2137 D(x): 0.5884 D(G(z)): 0.0000 / 0.0000 [12/1000][0/4] Loss_D: 0.2100 Loss_G: 11.7406 D(x): 0.9052 D(G(z)): 0.0043 / 0.0000 [13/1000][0/4] Loss_D: 0.0917 Loss_G: 5.3276 D(x): 0.9585 D(G(z)): 0.0026 / 0.0236 [14/1000][0/4] Loss_D: 0.3755 Loss_G: 8.7706 D(x): 0.9481 D(G(z)): 0.2118 / 0.0003 [15/1000][0/4] Loss_D: 2.9367 Loss_G: 10.9268 D(x): 0.1400 D(G(z)): 0.0000 / 0.0001 [16/1000][0/4] Loss_D: 0.1446 Loss_G: 3.3003 D(x): 0.9538 D(G(z)): 0.0693 / 0.0970 [17/1000][0/4] Loss_D: 1.4716 Loss_G: 9.4209 D(x): 0.9440 D(G(z)): 0.6716 / 0.0003 [18/1000][0/4] Loss_D: 0.7138 Loss_G: 4.4008 D(x): 0.6086 D(G(z)): 0.0265 / 0.0261 [19/1000][0/4] Loss_D: 0.6162 Loss_G: 2.8658 D(x): 0.6927 D(G(z)): 0.1197 / 0.0973 [20/1000][0/4] Loss_D: 0.3841 Loss_G: 7.0203 D(x): 0.7599 D(G(z)): 0.0243 / 0.0037 [21/1000][0/4] Loss_D: 0.5286 Loss_G: 3.0276 D(x): 0.7085 D(G(z)): 0.0764 / 0.0858 [22/1000][0/4] Loss_D: 0.3971 Loss_G: 4.1386 D(x): 0.8549 D(G(z)): 0.1716 / 0.0264 [23/1000][0/4] Loss_D: 0.4082 Loss_G: 4.6194 D(x): 0.8903 D(G(z)): 0.2205 / 0.0154 [24/1000][0/4] Loss_D: 0.2565 Loss_G: 2.6626 D(x): 0.8599 D(G(z)): 0.0766 / 0.0931 [25/1000][0/4] Loss_D: 0.2333 Loss_G: 4.6640 D(x): 0.8439 D(G(z)): 0.0338 / 0.0150 [26/1000][0/4] Loss_D: 0.1857 Loss_G: 3.8834 D(x): 0.8953 D(G(z)): 0.0548 / 0.0318 [27/1000][0/4] Loss_D: 0.2463 Loss_G: 4.2136 D(x): 0.9378 D(G(z)): 0.1550 / 0.0208 [28/1000][0/4] Loss_D: 0.1853 Loss_G: 4.1592 D(x): 0.9328 D(G(z)): 0.1003 / 0.0223 [29/1000][0/4] Loss_D: 0.2134 Loss_G: 3.0034 D(x): 0.8633 D(G(z)): 0.0471 / 0.0668 [30/1000][0/4] Loss_D: 0.2082 Loss_G: 3.5492 D(x): 0.8813 D(G(z)): 0.0682 / 0.0386 [31/1000][0/4] Loss_D: 0.1957 Loss_G: 4.2483 D(x): 0.9021 D(G(z)): 0.0807 / 0.0175 [32/1000][0/4] Loss_D: 1.4930 Loss_G: 17.5899 D(x): 0.9943 D(G(z)): 0.7484 / 0.0000 [33/1000][0/4] Loss_D: 3.7308 Loss_G: 1.6145 D(x): 0.1076 D(G(z)): 0.0035 / 0.3027 [34/1000][0/4] Loss_D: 0.2621 Loss_G: 3.5601 D(x): 0.9257 D(G(z)): 0.1453 / 0.0568 [35/1000][0/4] Loss_D: 0.4175 Loss_G: 4.6490 D(x): 0.9320 D(G(z)): 0.2702 / 0.0151 [36/1000][0/4] Loss_D: 0.1976 Loss_G: 3.8200 D(x): 0.8759 D(G(z)): 0.0503 / 0.0304 [37/1000][0/4] Loss_D: 0.3084 Loss_G: 4.0939 D(x): 0.9223 D(G(z)): 0.1901 / 0.0236 [38/1000][0/4] Loss_D: 0.3624 Loss_G: 2.5138 D(x): 0.7545 D(G(z)): 0.0460 / 0.1051 [39/1000][0/4] Loss_D: 0.2063 Loss_G: 5.6655 D(x): 0.9812 D(G(z)): 0.1620 / 0.0053 [40/1000][0/4] Loss_D: 0.2904 Loss_G: 3.0831 D(x): 0.8365 D(G(z)): 0.0865 / 0.0605 [41/1000][0/4] Loss_D: 0.1502 Loss_G: 4.2130 D(x): 0.9367 D(G(z)): 0.0751 / 0.0213 [42/1000][0/4] Loss_D: 0.1384 Loss_G: 3.6566 D(x): 0.8950 D(G(z)): 0.0209 / 0.0329 [43/1000][0/4] Loss_D: 0.2466 Loss_G: 5.0576 D(x): 0.9527 D(G(z)): 0.1687 / 0.0091 [44/1000][0/4] Loss_D: 0.6266 Loss_G: 0.3780 D(x): 0.5871 D(G(z)): 0.0092 / 0.6985 [45/1000][0/4] Loss_D: 0.4704 Loss_G: 6.5676 D(x): 0.9880 D(G(z)): 0.3252 / 0.0022 [46/1000][0/4] Loss_D: 0.1044 Loss_G: 2.4833 D(x): 0.9727 D(G(z)): 0.0706 / 0.1194 [47/1000][0/4] Loss_D: 0.3262 Loss_G: 4.2876 D(x): 0.9607 D(G(z)): 0.2371 / 0.0201 [48/1000][0/4] Loss_D: 0.2243 Loss_G: 4.5139 D(x): 0.8426 D(G(z)): 0.0327 / 0.0161 [49/1000][0/4] Loss_D: 0.1831 Loss_G: 5.2333 D(x): 0.9709 D(G(z)): 0.1370 / 0.0075 [50/1000][0/4] Loss_D: 0.1563 Loss_G: 4.6470 D(x): 0.9424 D(G(z)): 0.0876 / 0.0135 [51/1000][0/4] Loss_D: 0.3353 Loss_G: 2.0913 D(x): 0.7648 D(G(z)): 0.0300 / 0.1514 [52/1000][0/4] Loss_D: 0.2612 Loss_G: 7.2471 D(x): 0.9836 D(G(z)): 0.1976 / 0.0023 [53/1000][0/4] Loss_D: 1.0680 Loss_G: 4.0189 D(x): 0.4691 D(G(z)): 0.0013 / 0.0300 [54/1000][0/4] Loss_D: 0.2883 Loss_G: 2.9682 D(x): 0.8053 D(G(z)): 0.0366 / 0.0827 [55/1000][0/4] Loss_D: 0.8484 Loss_G: 4.4595 D(x): 0.5214 D(G(z)): 0.0019 / 0.0235 [56/1000][0/4] Loss_D: 0.1931 Loss_G: 4.3759 D(x): 0.9704 D(G(z)): 0.1319 / 0.0227 [57/1000][0/4] Loss_D: 1.6456 Loss_G: 1.1376 D(x): 0.3346 D(G(z)): 0.0032 / 0.3800 [58/1000][0/4] Loss_D: 0.2730 Loss_G: 4.1542 D(x): 0.8840 D(G(z)): 0.1011 / 0.0342 [59/1000][0/4] Loss_D: 0.6864 Loss_G: 1.2841 D(x): 0.6123 D(G(z)): 0.0475 / 0.3292 [60/1000][0/4] Loss_D: 0.3388 Loss_G: 4.3617 D(x): 0.9868 D(G(z)): 0.2475 / 0.0224 [61/1000][0/4] Loss_D: 0.4623 Loss_G: 2.4965 D(x): 0.7093 D(G(z)): 0.0500 / 0.1215 [62/1000][0/4] Loss_D: 1.4708 Loss_G: 3.1038 D(x): 0.3316 D(G(z)): 0.0039 / 0.0952 [63/1000][0/4] Loss_D: 0.3209 Loss_G: 3.4864 D(x): 0.9331 D(G(z)): 0.2019 / 0.0495 [64/1000][0/4] Loss_D: 0.3638 Loss_G: 2.4843 D(x): 0.7503 D(G(z)): 0.0436 / 0.1128 [65/1000][0/4] Loss_D: 0.4109 Loss_G: 5.5267 D(x): 0.9673 D(G(z)): 0.2900 / 0.0066 [66/1000][0/4] Loss_D: 0.2627 Loss_G: 3.0178 D(x): 0.8542 D(G(z)): 0.0789 / 0.0736 [67/1000][0/4] Loss_D: 0.4442 Loss_G: 2.5456 D(x): 0.7200 D(G(z)): 0.0556 / 0.1279 [68/1000][0/4] Loss_D: 0.3577 Loss_G: 2.4924 D(x): 0.7679 D(G(z)): 0.0419 / 0.1281 [69/1000][0/4] Loss_D: 0.9626 Loss_G: 1.2129 D(x): 0.4506 D(G(z)): 0.0073 / 0.3600 [70/1000][0/4] Loss_D: 4.6596 Loss_G: 0.9570 D(x): 0.0271 D(G(z)): 0.0011 / 0.5211 [71/1000][0/4] Loss_D: 0.8383 Loss_G: 5.6587 D(x): 0.9561 D(G(z)): 0.4657 / 0.0068 [72/1000][0/4] Loss_D: 0.3727 Loss_G: 3.2468 D(x): 0.8536 D(G(z)): 0.1646 / 0.0565 [73/1000][0/4] Loss_D: 0.4541 Loss_G: 4.8338 D(x): 0.9237 D(G(z)): 0.2827 / 0.0146 [74/1000][0/4] Loss_D: 0.3473 Loss_G: 2.8330 D(x): 0.8334 D(G(z)): 0.1142 / 0.0914 [75/1000][0/4] Loss_D: 0.3199 Loss_G: 2.9908 D(x): 0.8023 D(G(z)): 0.0714 / 0.0723 [76/1000][0/4] Loss_D: 0.3228 Loss_G: 3.7281 D(x): 0.8675 D(G(z)): 0.1440 / 0.0349 [77/1000][0/4] Loss_D: 0.4410 Loss_G: 4.6912 D(x): 0.8766 D(G(z)): 0.2457 / 0.0142 [78/1000][0/4] Loss_D: 1.3767 Loss_G: 6.8126 D(x): 0.9914 D(G(z)): 0.6764 / 0.0023 [79/1000][0/4] Loss_D: 0.4919 Loss_G: 2.9949 D(x): 0.6969 D(G(z)): 0.0514 / 0.0838 [80/1000][0/4] Loss_D: 0.6816 Loss_G: 1.8426 D(x): 0.5685 D(G(z)): 0.0199 / 0.2152 [81/1000][0/4] Loss_D: 0.2666 Loss_G: 5.0439 D(x): 0.9348 D(G(z)): 0.1658 / 0.0096 [82/1000][0/4] Loss_D: 1.4857 Loss_G: 8.1452 D(x): 0.9911 D(G(z)): 0.7167 / 0.0007 [83/1000][0/4] Loss_D: 0.2499 Loss_G: 2.7396 D(x): 0.8640 D(G(z)): 0.0820 / 0.1029 [84/1000][0/4] Loss_D: 0.5257 Loss_G: 2.8737 D(x): 0.6476 D(G(z)): 0.0267 / 0.0791 [85/1000][0/4] Loss_D: 0.3957 Loss_G: 3.9161 D(x): 0.9111 D(G(z)): 0.2386 / 0.0284 [86/1000][0/4] Loss_D: 1.1209 Loss_G: 6.6056 D(x): 0.9868 D(G(z)): 0.6240 / 0.0031 [87/1000][0/4] Loss_D: 0.2919 Loss_G: 3.0157 D(x): 0.8137 D(G(z)): 0.0628 / 0.0766 [88/1000][0/4] Loss_D: 0.4273 Loss_G: 3.9696 D(x): 0.9067 D(G(z)): 0.2604 / 0.0269 [89/1000][0/4] Loss_D: 0.8909 Loss_G: 6.4159 D(x): 0.9796 D(G(z)): 0.5346 / 0.0028 [90/1000][0/4] Loss_D: 0.3025 Loss_G: 3.0488 D(x): 0.9095 D(G(z)): 0.1688 / 0.0720 [91/1000][0/4] Loss_D: 0.3899 Loss_G: 2.2378 D(x): 0.7378 D(G(z)): 0.0589 / 0.1434 [92/1000][0/4] Loss_D: 0.3327 Loss_G: 2.8739 D(x): 0.7663 D(G(z)): 0.0432 / 0.0784 [93/1000][0/4] Loss_D: 0.4802 Loss_G: 5.1903 D(x): 0.9400 D(G(z)): 0.3092 / 0.0105 [94/1000][0/4] Loss_D: 0.2770 Loss_G: 2.6110 D(x): 0.8966 D(G(z)): 0.1332 / 0.1102 [95/1000][0/4] Loss_D: 0.3264 Loss_G: 3.4158 D(x): 0.8870 D(G(z)): 0.1653 / 0.0536 [96/1000][0/4] Loss_D: 0.2910 Loss_G: 2.2766 D(x): 0.8233 D(G(z)): 0.0787 / 0.1313 [97/1000][0/4] Loss_D: 2.2393 Loss_G: 1.9751 D(x): 0.1650 D(G(z)): 0.0039 / 0.2044 [98/1000][0/4] Loss_D: 0.7914 Loss_G: 3.8871 D(x): 0.5681 D(G(z)): 0.0531 / 0.0420 [99/1000][0/4] Loss_D: 1.5368 Loss_G: 5.1121 D(x): 0.9736 D(G(z)): 0.7366 / 0.0144 [100/1000][0/4] Loss_D: 0.3957 Loss_G: 2.4391 D(x): 0.7738 D(G(z)): 0.1044 / 0.1141 [101/1000][0/4] Loss_D: 0.4001 Loss_G: 2.6844 D(x): 0.8291 D(G(z)): 0.1734 / 0.0855 [102/1000][0/4] Loss_D: 0.5685 Loss_G: 1.3473 D(x): 0.6577 D(G(z)): 0.0936 / 0.2951 [103/1000][0/4] Loss_D: 0.3209 Loss_G: 3.3052 D(x): 0.8201 D(G(z)): 0.1025 / 0.0481 [104/1000][0/4] Loss_D: 0.3352 Loss_G: 2.5980 D(x): 0.8537 D(G(z)): 0.1505 / 0.0952 [105/1000][0/4] Loss_D: 0.4755 Loss_G: 4.6078 D(x): 0.9432 D(G(z)): 0.3177 / 0.0148 [106/1000][0/4] Loss_D: 0.6790 Loss_G: 4.9194 D(x): 0.9838 D(G(z)): 0.4372 / 0.0113 [107/1000][0/4] Loss_D: 2.1461 Loss_G: 6.1552 D(x): 0.9973 D(G(z)): 0.8317 / 0.0040 [108/1000][0/4] Loss_D: 0.6480 Loss_G: 1.9052 D(x): 0.5810 D(G(z)): 0.0329 / 0.1844 [109/1000][0/4] Loss_D: 0.3370 Loss_G: 3.2436 D(x): 0.9018 D(G(z)): 0.1932 / 0.0531 [110/1000][0/4] Loss_D: 0.3842 Loss_G: 2.4904 D(x): 0.8135 D(G(z)): 0.1490 / 0.1045 [111/1000][0/4] Loss_D: 0.3631 Loss_G: 2.5002 D(x): 0.7750 D(G(z)): 0.0851 / 0.1061 [112/1000][0/4] Loss_D: 0.3537 Loss_G: 3.5618 D(x): 0.8816 D(G(z)): 0.1875 / 0.0408 [113/1000][0/4] Loss_D: 0.3315 Loss_G: 3.6177 D(x): 0.8998 D(G(z)): 0.1877 / 0.0381 [114/1000][0/4] Loss_D: 0.3580 Loss_G: 1.8256 D(x): 0.7502 D(G(z)): 0.0478 / 0.1895 [115/1000][0/4] Loss_D: 2.2498 Loss_G: 1.9888 D(x): 0.1473 D(G(z)): 0.0018 / 0.1921 [116/1000][0/4] Loss_D: 2.0357 Loss_G: 0.9320 D(x): 0.1808 D(G(z)): 0.0049 / 0.4642 [117/1000][0/4] Loss_D: 0.7428 Loss_G: 4.7057 D(x): 0.9488 D(G(z)): 0.4648 / 0.0135 [118/1000][0/4] Loss_D: 0.5284 Loss_G: 4.0327 D(x): 0.9276 D(G(z)): 0.3456 / 0.0245 [119/1000][0/4] Loss_D: 0.3691 Loss_G: 3.4717 D(x): 0.9038 D(G(z)): 0.2235 / 0.0392 [120/1000][0/4] Loss_D: 0.4503 Loss_G: 4.2273 D(x): 0.9347 D(G(z)): 0.3025 / 0.0189 [121/1000][0/4] Loss_D: 0.3135 Loss_G: 2.8060 D(x): 0.8491 D(G(z)): 0.1287 / 0.0760 [122/1000][0/4] Loss_D: 0.3103 Loss_G: 3.3234 D(x): 0.8494 D(G(z)): 0.1253 / 0.0492 [123/1000][0/4] Loss_D: 0.6839 Loss_G: 6.3258 D(x): 0.9670 D(G(z)): 0.4566 / 0.0032 [124/1000][0/4] Loss_D: 0.3738 Loss_G: 3.7806 D(x): 0.9726 D(G(z)): 0.2727 / 0.0331 [125/1000][0/4] Loss_D: 0.3806 Loss_G: 4.7427 D(x): 0.9381 D(G(z)): 0.2579 / 0.0115 [126/1000][0/4] Loss_D: 0.2153 Loss_G: 2.6760 D(x): 0.9137 D(G(z)): 0.1111 / 0.0894 [127/1000][0/4] Loss_D: 0.2406 Loss_G: 3.6697 D(x): 0.9271 D(G(z)): 0.1445 / 0.0349 [128/1000][0/4] Loss_D: 0.3061 Loss_G: 2.8573 D(x): 0.8525 D(G(z)): 0.1266 / 0.0738 [129/1000][0/4] Loss_D: 0.3216 Loss_G: 2.3409 D(x): 0.8002 D(G(z)): 0.0828 / 0.1209 [130/1000][0/4] Loss_D: 0.8374 Loss_G: 1.3866 D(x): 0.4777 D(G(z)): 0.0142 / 0.2950 [131/1000][0/4] Loss_D: 0.2813 Loss_G: 3.2093 D(x): 0.8484 D(G(z)): 0.0962 / 0.0564 [132/1000][0/4] Loss_D: 0.3291 Loss_G: 2.9375 D(x): 0.8446 D(G(z)): 0.1363 / 0.0674 [133/1000][0/4] Loss_D: 0.3512 Loss_G: 3.3843 D(x): 0.8627 D(G(z)): 0.1738 / 0.0423 [134/1000][0/4] Loss_D: 0.4190 Loss_G: 5.6182 D(x): 0.9611 D(G(z)): 0.2961 / 0.0065 [135/1000][0/4] Loss_D: 0.4672 Loss_G: 2.6607 D(x): 0.8317 D(G(z)): 0.1964 / 0.0954 [136/1000][0/4] Loss_D: 1.7680 Loss_G: 1.5962 D(x): 0.2255 D(G(z)): 0.0067 / 0.2674 [137/1000][0/4] Loss_D: 0.8131 Loss_G: 5.3189 D(x): 0.8467 D(G(z)): 0.4321 / 0.0079 [138/1000][0/4] Loss_D: 0.3109 Loss_G: 2.1031 D(x): 0.8404 D(G(z)): 0.1142 / 0.1548 [139/1000][0/4] Loss_D: 0.2722 Loss_G: 4.4246 D(x): 0.8850 D(G(z)): 0.1239 / 0.0174 [140/1000][0/4] Loss_D: 0.4293 Loss_G: 2.9562 D(x): 0.9002 D(G(z)): 0.2567 / 0.0673 [141/1000][0/4] Loss_D: 0.5973 Loss_G: 1.7873 D(x): 0.6065 D(G(z)): 0.0489 / 0.2071 [142/1000][0/4] Loss_D: 0.3424 Loss_G: 3.0054 D(x): 0.8155 D(G(z)): 0.1121 / 0.0662 [143/1000][0/4] Loss_D: 0.4124 Loss_G: 3.3366 D(x): 0.8827 D(G(z)): 0.2355 / 0.0470 [144/1000][0/4] Loss_D: 0.8345 Loss_G: 5.5943 D(x): 0.9733 D(G(z)): 0.5133 / 0.0070 [145/1000][0/4] Loss_D: 0.2829 Loss_G: 2.6988 D(x): 0.8721 D(G(z)): 0.1260 / 0.0849 [146/1000][0/4] Loss_D: 0.3824 Loss_G: 2.0371 D(x): 0.7799 D(G(z)): 0.1087 / 0.1567 [147/1000][0/4] Loss_D: 0.7043 Loss_G: 1.4657 D(x): 0.5352 D(G(z)): 0.0171 / 0.2690 [148/1000][0/4] Loss_D: 0.2576 Loss_G: 3.1980 D(x): 0.8720 D(G(z)): 0.1048 / 0.0590 [149/1000][0/4] Loss_D: 0.4613 Loss_G: 4.4346 D(x): 0.9056 D(G(z)): 0.2834 / 0.0179 [150/1000][0/4] Loss_D: 1.2256 Loss_G: 8.4685 D(x): 0.9922 D(G(z)): 0.6455 / 0.0004 [151/1000][0/4] Loss_D: 0.3647 Loss_G: 1.9762 D(x): 0.7773 D(G(z)): 0.0773 / 0.1842 [152/1000][0/4] Loss_D: 0.5576 Loss_G: 4.1067 D(x): 0.9018 D(G(z)): 0.3349 / 0.0232 [153/1000][0/4] Loss_D: 0.5249 Loss_G: 3.4100 D(x): 0.9343 D(G(z)): 0.3430 / 0.0432 [154/1000][0/4] Loss_D: 0.3824 Loss_G: 2.4822 D(x): 0.8045 D(G(z)): 0.1350 / 0.1014 [155/1000][0/4] Loss_D: 0.7657 Loss_G: 5.5089 D(x): 0.9566 D(G(z)): 0.4872 / 0.0059 [156/1000][0/4] Loss_D: 0.3240 Loss_G: 2.2223 D(x): 0.7668 D(G(z)): 0.0386 / 0.1408 [157/1000][0/4] Loss_D: 0.3788 Loss_G: 3.4786 D(x): 0.9194 D(G(z)): 0.2385 / 0.0404 [158/1000][0/4] Loss_D: 0.4530 Loss_G: 3.6647 D(x): 0.9035 D(G(z)): 0.2781 / 0.0335 [159/1000][0/4] Loss_D: 0.3675 Loss_G: 3.8356 D(x): 0.9411 D(G(z)): 0.2482 / 0.0296 [160/1000][0/4] Loss_D: 0.6028 Loss_G: 5.1621 D(x): 0.9636 D(G(z)): 0.4014 / 0.0094 [161/1000][0/4] Loss_D: 1.0819 Loss_G: 6.4298 D(x): 0.9895 D(G(z)): 0.6072 / 0.0031 [162/1000][0/4] Loss_D: 0.3328 Loss_G: 3.6497 D(x): 0.9409 D(G(z)): 0.2247 / 0.0359 [163/1000][0/4] Loss_D: 0.2893 Loss_G: 3.2135 D(x): 0.8821 D(G(z)): 0.1418 / 0.0553 [164/1000][0/4] Loss_D: 0.3118 Loss_G: 3.7017 D(x): 0.9090 D(G(z)): 0.1868 / 0.0307 [165/1000][0/4] Loss_D: 0.6886 Loss_G: 6.6013 D(x): 0.9661 D(G(z)): 0.4504 / 0.0025 [166/1000][0/4] Loss_D: 0.5941 Loss_G: 6.1662 D(x): 0.9725 D(G(z)): 0.4002 / 0.0032 [167/1000][0/4] Loss_D: 0.6094 Loss_G: 1.4198 D(x): 0.6286 D(G(z)): 0.0578 / 0.3041 [168/1000][0/4] Loss_D: 0.6028 Loss_G: 1.9366 D(x): 0.6531 D(G(z)): 0.1217 / 0.1847 [169/1000][0/4] Loss_D: 1.0126 Loss_G: 1.3864 D(x): 0.4278 D(G(z)): 0.0298 / 0.3053 [170/1000][0/4] Loss_D: 0.5637 Loss_G: 3.9594 D(x): 0.9479 D(G(z)): 0.3676 / 0.0285 [171/1000][0/4] Loss_D: 0.4115 Loss_G: 2.6668 D(x): 0.8424 D(G(z)): 0.1972 / 0.0871 [172/1000][0/4] Loss_D: 0.3529 Loss_G: 2.2878 D(x): 0.8293 D(G(z)): 0.1416 / 0.1217 [173/1000][0/4] Loss_D: 0.3603 Loss_G: 2.0036 D(x): 0.7743 D(G(z)): 0.0861 / 0.1564 [174/1000][0/4] Loss_D: 0.3082 Loss_G: 3.2452 D(x): 0.8989 D(G(z)): 0.1737 / 0.0480 [175/1000][0/4] Loss_D: 0.3428 Loss_G: 3.1809 D(x): 0.9202 D(G(z)): 0.2145 / 0.0564 [176/1000][0/4] Loss_D: 0.3107 Loss_G: 2.4054 D(x): 0.7875 D(G(z)): 0.0583 / 0.1146 [177/1000][0/4] Loss_D: 0.2469 Loss_G: 3.1919 D(x): 0.8924 D(G(z)): 0.1179 / 0.0557 [178/1000][0/4] Loss_D: 0.3579 Loss_G: 1.6311 D(x): 0.7449 D(G(z)): 0.0451 / 0.2295 [179/1000][0/4] Loss_D: 0.2450 Loss_G: 3.5441 D(x): 0.8760 D(G(z)): 0.0979 / 0.0395 [180/1000][0/4] Loss_D: 0.2848 Loss_G: 2.4151 D(x): 0.8504 D(G(z)): 0.1052 / 0.1136 [181/1000][0/4] Loss_D: 0.6642 Loss_G: 0.9362 D(x): 0.5544 D(G(z)): 0.0180 / 0.4401 [182/1000][0/4] Loss_D: 1.4925 Loss_G: 1.1317 D(x): 0.2933 D(G(z)): 0.0106 / 0.4412 [183/1000][0/4] Loss_D: 0.5422 Loss_G: 3.9119 D(x): 0.9522 D(G(z)): 0.3553 / 0.0280 [184/1000][0/4] Loss_D: 0.3635 Loss_G: 3.1620 D(x): 0.8684 D(G(z)): 0.1835 / 0.0565 [185/1000][0/4] Loss_D: 0.5787 Loss_G: 5.1990 D(x): 0.9513 D(G(z)): 0.3829 / 0.0088 [186/1000][0/4] Loss_D: 0.2667 Loss_G: 2.7125 D(x): 0.8668 D(G(z)): 0.1080 / 0.0847 [187/1000][0/4] Loss_D: 0.3628 Loss_G: 4.3377 D(x): 0.9490 D(G(z)): 0.2515 / 0.0181 [188/1000][0/4] Loss_D: 0.2609 Loss_G: 2.6256 D(x): 0.8531 D(G(z)): 0.0892 / 0.0897 [189/1000][0/4] Loss_D: 0.3054 Loss_G: 2.8349 D(x): 0.8620 D(G(z)): 0.1352 / 0.0754 [190/1000][0/4] Loss_D: 0.3032 Loss_G: 2.2329 D(x): 0.7943 D(G(z)): 0.0608 / 0.1390 [191/1000][0/4] Loss_D: 0.3142 Loss_G: 2.3438 D(x): 0.7809 D(G(z)): 0.0541 / 0.1264 [192/1000][0/4] Loss_D: 0.2348 Loss_G: 2.9159 D(x): 0.8995 D(G(z)): 0.1148 / 0.0701 [193/1000][0/4] Loss_D: 0.3601 Loss_G: 2.0481 D(x): 0.7354 D(G(z)): 0.0367 / 0.1615 [194/1000][0/4] Loss_D: 0.2446 Loss_G: 2.8631 D(x): 0.8326 D(G(z)): 0.0506 / 0.0758 [195/1000][0/4] Loss_D: 0.2646 Loss_G: 3.7236 D(x): 0.9264 D(G(z)): 0.1603 / 0.0341 [196/1000][0/4] Loss_D: 0.4845 Loss_G: 6.2827 D(x): 0.9688 D(G(z)): 0.3433 / 0.0037 [197/1000][0/4] Loss_D: 1.3456 Loss_G: 6.2629 D(x): 0.9789 D(G(z)): 0.6646 / 0.0075 [198/1000][0/4] Loss_D: 0.5185 Loss_G: 1.9791 D(x): 0.7320 D(G(z)): 0.1419 / 0.1899 [199/1000][0/4] Loss_D: 0.4572 Loss_G: 2.3482 D(x): 0.6960 D(G(z)): 0.0578 / 0.1216 [200/1000][0/4] Loss_D: 0.3971 Loss_G: 3.0835 D(x): 0.8106 D(G(z)): 0.1539 / 0.0615 [201/1000][0/4] Loss_D: 1.0548 Loss_G: 1.9632 D(x): 0.4074 D(G(z)): 0.0109 / 0.2030 [202/1000][0/4] Loss_D: 0.2974 Loss_G: 2.8623 D(x): 0.8789 D(G(z)): 0.1439 / 0.0777 [203/1000][0/4] Loss_D: 0.4859 Loss_G: 1.8554 D(x): 0.6682 D(G(z)): 0.0533 / 0.1962 [204/1000][0/4] Loss_D: 0.2209 Loss_G: 3.7317 D(x): 0.8929 D(G(z)): 0.0957 / 0.0340 [205/1000][0/4] Loss_D: 0.3083 Loss_G: 2.9704 D(x): 0.8634 D(G(z)): 0.1391 / 0.0661 [206/1000][0/4] Loss_D: 0.2159 Loss_G: 3.3805 D(x): 0.9178 D(G(z)): 0.1170 / 0.0429 [207/1000][0/4] Loss_D: 0.2017 Loss_G: 3.0466 D(x): 0.9134 D(G(z)): 0.1008 / 0.0602 [208/1000][0/4] Loss_D: 0.3454 Loss_G: 2.0816 D(x): 0.7557 D(G(z)): 0.0494 / 0.1574 [209/1000][0/4] Loss_D: 0.2635 Loss_G: 3.5657 D(x): 0.9183 D(G(z)): 0.1557 / 0.0374 [210/1000][0/4] Loss_D: 0.2809 Loss_G: 2.4198 D(x): 0.8316 D(G(z)): 0.0830 / 0.1181 [211/1000][0/4] Loss_D: 0.2926 Loss_G: 2.8836 D(x): 0.8102 D(G(z)): 0.0688 / 0.0785 [212/1000][0/4] Loss_D: 0.3045 Loss_G: 4.3193 D(x): 0.9629 D(G(z)): 0.2225 / 0.0179 [213/1000][0/4] Loss_D: 0.5075 Loss_G: 4.8523 D(x): 0.9718 D(G(z)): 0.3498 / 0.0127 [214/1000][0/4] Loss_D: 0.2628 Loss_G: 3.7413 D(x): 0.9680 D(G(z)): 0.1936 / 0.0329 [215/1000][0/4] Loss_D: 0.2461 Loss_G: 2.9695 D(x): 0.8544 D(G(z)): 0.0783 / 0.0694 [216/1000][0/4] Loss_D: 0.3997 Loss_G: 5.8158 D(x): 0.9679 D(G(z)): 0.2915 / 0.0053 [217/1000][0/4] Loss_D: 0.4788 Loss_G: 5.3602 D(x): 0.9792 D(G(z)): 0.3176 / 0.0088 [218/1000][0/4] Loss_D: 1.4741 Loss_G: 4.4994 D(x): 0.9929 D(G(z)): 0.6608 / 0.0237 [219/1000][0/4] Loss_D: 0.6717 Loss_G: 1.9927 D(x): 0.6365 D(G(z)): 0.1091 / 0.1805 [220/1000][0/4] Loss_D: 0.6911 Loss_G: 2.3736 D(x): 0.5543 D(G(z)): 0.0292 / 0.1393 [221/1000][0/4] Loss_D: 0.4004 Loss_G: 2.8901 D(x): 0.8223 D(G(z)): 0.1658 / 0.0748 [222/1000][0/4] Loss_D: 0.6871 Loss_G: 1.4338 D(x): 0.5575 D(G(z)): 0.0296 / 0.2823 [223/1000][0/4] Loss_D: 0.3863 Loss_G: 3.9859 D(x): 0.9578 D(G(z)): 0.2723 / 0.0259 [224/1000][0/4] Loss_D: 0.2963 Loss_G: 3.2701 D(x): 0.8941 D(G(z)): 0.1601 / 0.0486 [225/1000][0/4] Loss_D: 0.3037 Loss_G: 3.5607 D(x): 0.9296 D(G(z)): 0.1943 / 0.0408 [226/1000][0/4] Loss_D: 0.2207 Loss_G: 3.3360 D(x): 0.9080 D(G(z)): 0.1109 / 0.0480 [227/1000][0/4] Loss_D: 0.2365 Loss_G: 3.1492 D(x): 0.8848 D(G(z)): 0.1012 / 0.0595 [228/1000][0/4] Loss_D: 0.2772 Loss_G: 2.0817 D(x): 0.8233 D(G(z)): 0.0715 / 0.1642 [229/1000][0/4] Loss_D: 0.2611 Loss_G: 2.7923 D(x): 0.8498 D(G(z)): 0.0845 / 0.0892 [230/1000][0/4] Loss_D: 0.2457 Loss_G: 3.5511 D(x): 0.9342 D(G(z)): 0.1550 / 0.0382 [231/1000][0/4] Loss_D: 0.2116 Loss_G: 3.0758 D(x): 0.9063 D(G(z)): 0.1021 / 0.0606 [232/1000][0/4] Loss_D: 0.2543 Loss_G: 3.9233 D(x): 0.9542 D(G(z)): 0.1761 / 0.0282 [233/1000][0/4] Loss_D: 0.8280 Loss_G: 6.5919 D(x): 0.9889 D(G(z)): 0.5042 / 0.0030 [234/1000][0/4] Loss_D: 0.8261 Loss_G: 5.5919 D(x): 0.9932 D(G(z)): 0.5037 / 0.0066 [235/1000][0/4] Loss_D: 0.2548 Loss_G: 3.0801 D(x): 0.8736 D(G(z)): 0.1044 / 0.0630 [236/1000][0/4] Loss_D: 0.2717 Loss_G: 3.2120 D(x): 0.8843 D(G(z)): 0.1288 / 0.0569 [237/1000][0/4] Loss_D: 0.2390 Loss_G: 2.9544 D(x): 0.8896 D(G(z)): 0.1081 / 0.0691 [238/1000][0/4] Loss_D: 0.2339 Loss_G: 2.8514 D(x): 0.8597 D(G(z)): 0.0725 / 0.0743 [239/1000][0/4] Loss_D: 0.2149 Loss_G: 3.0060 D(x): 0.8927 D(G(z)): 0.0916 / 0.0641 [240/1000][0/4] Loss_D: 0.2032 Loss_G: 3.2894 D(x): 0.9116 D(G(z)): 0.0988 / 0.0498 [241/1000][0/4] Loss_D: 0.2669 Loss_G: 4.4815 D(x): 0.9531 D(G(z)): 0.1874 / 0.0162 [242/1000][0/4] Loss_D: 1.9386 Loss_G: 11.0033 D(x): 0.9963 D(G(z)): 0.8004 / 0.0000 [243/1000][0/4] Loss_D: 0.6158 Loss_G: 3.1419 D(x): 0.9463 D(G(z)): 0.3576 / 0.0738 [244/1000][0/4] Loss_D: 0.8310 Loss_G: 6.1417 D(x): 0.9394 D(G(z)): 0.4963 / 0.0046 [245/1000][0/4] Loss_D: 0.3766 Loss_G: 2.1881 D(x): 0.7505 D(G(z)): 0.0594 / 0.1469 [246/1000][0/4] Loss_D: 0.5114 Loss_G: 2.2484 D(x): 0.6920 D(G(z)): 0.0970 / 0.1381 [247/1000][0/4] Loss_D: 0.4051 Loss_G: 2.6038 D(x): 0.7265 D(G(z)): 0.0547 / 0.0981 [248/1000][0/4] Loss_D: 0.4064 Loss_G: 3.5948 D(x): 0.8764 D(G(z)): 0.2213 / 0.0384 [249/1000][0/4] Loss_D: 0.4461 Loss_G: 4.2828 D(x): 0.9502 D(G(z)): 0.3055 / 0.0194 [250/1000][0/4] Loss_D: 0.2850 Loss_G: 2.9856 D(x): 0.8262 D(G(z)): 0.0762 / 0.0710 [251/1000][0/4] Loss_D: 0.3356 Loss_G: 4.1299 D(x): 0.9456 D(G(z)): 0.2290 / 0.0232 [252/1000][0/4] Loss_D: 0.2319 Loss_G: 3.2421 D(x): 0.9035 D(G(z)): 0.1161 / 0.0515 [253/1000][0/4] Loss_D: 0.2915 Loss_G: 3.5196 D(x): 0.9312 D(G(z)): 0.1865 / 0.0415 [254/1000][0/4] Loss_D: 0.2640 Loss_G: 2.8730 D(x): 0.8782 D(G(z)): 0.1176 / 0.0738 [255/1000][0/4] Loss_D: 0.3248 Loss_G: 3.8766 D(x): 0.9561 D(G(z)): 0.2290 / 0.0286 [256/1000][0/4] Loss_D: 0.2419 Loss_G: 2.6468 D(x): 0.8579 D(G(z)): 0.0786 / 0.0898 [257/1000][0/4] Loss_D: 0.2533 Loss_G: 2.6411 D(x): 0.8340 D(G(z)): 0.0611 / 0.0998 [258/1000][0/4] Loss_D: 0.2114 Loss_G: 3.1647 D(x): 0.8756 D(G(z)): 0.0701 / 0.0596 [259/1000][0/4] Loss_D: 0.2289 Loss_G: 3.2526 D(x): 0.9122 D(G(z)): 0.1219 / 0.0519 [260/1000][0/4] Loss_D: 0.2190 Loss_G: 2.9376 D(x): 0.9002 D(G(z)): 0.1028 / 0.0709 [261/1000][0/4] Loss_D: 0.2881 Loss_G: 2.0184 D(x): 0.7895 D(G(z)): 0.0404 / 0.1718 [262/1000][0/4] Loss_D: 0.2182 Loss_G: 3.0643 D(x): 0.8384 D(G(z)): 0.0348 / 0.0657 [263/1000][0/4] Loss_D: 0.2826 Loss_G: 4.3611 D(x): 0.9545 D(G(z)): 0.1975 / 0.0190 [264/1000][0/4] Loss_D: 0.8386 Loss_G: 7.6109 D(x): 0.9898 D(G(z)): 0.5055 / 0.0011 [265/1000][0/4] Loss_D: 2.6147 Loss_G: 11.6499 D(x): 0.9975 D(G(z)): 0.8752 / 0.0001 [266/1000][0/4] Loss_D: 0.8841 Loss_G: 2.7384 D(x): 0.7386 D(G(z)): 0.3432 / 0.1031 [267/1000][0/4] Loss_D: 0.9015 Loss_G: 5.6110 D(x): 0.9335 D(G(z)): 0.5113 / 0.0084 [268/1000][0/4] Loss_D: 0.4849 Loss_G: 3.0185 D(x): 0.9411 D(G(z)): 0.3066 / 0.0756 [269/1000][0/4] Loss_D: 0.4039 Loss_G: 3.0318 D(x): 0.7970 D(G(z)): 0.1392 / 0.0703 [270/1000][0/4] Loss_D: 0.4246 Loss_G: 3.9975 D(x): 0.9038 D(G(z)): 0.2558 / 0.0260 [271/1000][0/4] Loss_D: 0.3329 Loss_G: 2.7577 D(x): 0.8416 D(G(z)): 0.1361 / 0.0833 [272/1000][0/4] Loss_D: 0.2863 Loss_G: 2.9463 D(x): 0.8264 D(G(z)): 0.0798 / 0.0710 [273/1000][0/4] Loss_D: 0.2609 Loss_G: 2.7029 D(x): 0.8425 D(G(z)): 0.0778 / 0.0885 [274/1000][0/4] Loss_D: 0.3834 Loss_G: 2.7665 D(x): 0.7309 D(G(z)): 0.0484 / 0.0958 [275/1000][0/4] Loss_D: 0.2359 Loss_G: 2.9236 D(x): 0.8995 D(G(z)): 0.1156 / 0.0693 [276/1000][0/4] Loss_D: 0.2690 Loss_G: 2.7714 D(x): 0.8661 D(G(z)): 0.1098 / 0.0830 [277/1000][0/4] Loss_D: 0.2494 Loss_G: 3.1851 D(x): 0.8363 D(G(z)): 0.0607 / 0.0594 [278/1000][0/4] Loss_D: 0.2471 Loss_G: 2.9686 D(x): 0.9060 D(G(z)): 0.1312 / 0.0670 [279/1000][0/4] Loss_D: 0.3917 Loss_G: 4.6255 D(x): 0.9585 D(G(z)): 0.2782 / 0.0140 [280/1000][0/4] Loss_D: 0.1973 Loss_G: 2.7095 D(x): 0.8821 D(G(z)): 0.0633 / 0.0912 [281/1000][0/4] Loss_D: 0.2768 Loss_G: 3.9687 D(x): 0.9507 D(G(z)): 0.1890 / 0.0275 [282/1000][0/4] Loss_D: 0.2323 Loss_G: 2.8852 D(x): 0.8436 D(G(z)): 0.0528 / 0.0740 [283/1000][0/4] Loss_D: 0.2294 Loss_G: 2.9188 D(x): 0.8843 D(G(z)): 0.0950 / 0.0725 [284/1000][0/4] Loss_D: 0.3089 Loss_G: 4.3567 D(x): 0.9533 D(G(z)): 0.2179 / 0.0184 [285/1000][0/4] Loss_D: 0.1961 Loss_G: 3.1136 D(x): 0.9334 D(G(z)): 0.1144 / 0.0587 [286/1000][0/4] Loss_D: 0.2278 Loss_G: 2.5471 D(x): 0.8645 D(G(z)): 0.0713 / 0.1030 [287/1000][0/4] Loss_D: 0.3631 Loss_G: 2.0761 D(x): 0.7243 D(G(z)): 0.0223 / 0.1684 [288/1000][0/4] Loss_D: 0.2402 Loss_G: 2.8460 D(x): 0.8582 D(G(z)): 0.0775 / 0.0774 [289/1000][0/4] Loss_D: 0.6037 Loss_G: 6.6981 D(x): 0.9799 D(G(z)): 0.4131 / 0.0023 [290/1000][0/4] Loss_D: 1.7911 Loss_G: 7.4561 D(x): 0.9951 D(G(z)): 0.7612 / 0.0016 [291/1000][0/4] Loss_D: 0.4199 Loss_G: 2.9089 D(x): 0.9390 D(G(z)): 0.2614 / 0.0877 [292/1000][0/4] Loss_D: 0.6545 Loss_G: 1.9033 D(x): 0.6088 D(G(z)): 0.0741 / 0.1984 [293/1000][0/4] Loss_D: 0.3066 Loss_G: 3.4130 D(x): 0.9062 D(G(z)): 0.1732 / 0.0461 [294/1000][0/4] Loss_D: 0.3053 Loss_G: 2.9706 D(x): 0.8622 D(G(z)): 0.1331 / 0.0688 [295/1000][0/4] Loss_D: 0.2711 Loss_G: 2.5180 D(x): 0.8653 D(G(z)): 0.1098 / 0.1005 [296/1000][0/4] Loss_D: 0.2585 Loss_G: 3.7271 D(x): 0.9381 D(G(z)): 0.1674 / 0.0337 [297/1000][0/4] Loss_D: 0.2318 Loss_G: 2.6222 D(x): 0.8596 D(G(z)): 0.0706 / 0.0952 [298/1000][0/4] Loss_D: 0.3529 Loss_G: 4.3887 D(x): 0.9592 D(G(z)): 0.2504 / 0.0183 [299/1000][0/4] Loss_D: 0.2345 Loss_G: 2.5007 D(x): 0.8458 D(G(z)): 0.0565 / 0.1106 [300/1000][0/4] Loss_D: 0.2817 Loss_G: 2.3148 D(x): 0.8382 D(G(z)): 0.0910 / 0.1293 [301/1000][0/4] Loss_D: 0.2505 Loss_G: 3.4925 D(x): 0.9138 D(G(z)): 0.1392 / 0.0408 [302/1000][0/4] Loss_D: 0.2316 Loss_G: 3.8494 D(x): 0.9418 D(G(z)): 0.1514 / 0.0290 [303/1000][0/4] Loss_D: 0.2221 Loss_G: 3.6135 D(x): 0.9426 D(G(z)): 0.1436 / 0.0376 [304/1000][0/4] Loss_D: 0.2280 Loss_G: 3.3787 D(x): 0.9098 D(G(z)): 0.1182 / 0.0466 [305/1000][0/4] Loss_D: 0.2323 Loss_G: 4.2471 D(x): 0.9610 D(G(z)): 0.1658 / 0.0205 [306/1000][0/4] Loss_D: 0.4358 Loss_G: 1.6471 D(x): 0.6731 D(G(z)): 0.0138 / 0.2359 [307/1000][0/4] Loss_D: 1.3311 Loss_G: 0.4563 D(x): 0.3353 D(G(z)): 0.0052 / 0.6993 [308/1000][0/4] Loss_D: 0.1942 Loss_G: 4.8511 D(x): 0.9398 D(G(z)): 0.1117 / 0.0150 [309/1000][0/4] Loss_D: 0.2977 Loss_G: 3.3320 D(x): 0.8939 D(G(z)): 0.1554 / 0.0538 [310/1000][0/4] Loss_D: 0.4294 Loss_G: 4.3553 D(x): 0.9319 D(G(z)): 0.2789 / 0.0190 [311/1000][0/4] Loss_D: 0.2648 Loss_G: 2.4971 D(x): 0.8177 D(G(z)): 0.0520 / 0.1054 [312/1000][0/4] Loss_D: 0.3395 Loss_G: 4.4355 D(x): 0.9703 D(G(z)): 0.2495 / 0.0171 [313/1000][0/4] Loss_D: 0.2398 Loss_G: 4.4170 D(x): 0.9451 D(G(z)): 0.1577 / 0.0178 [314/1000][0/4] Loss_D: 0.2129 Loss_G: 2.5913 D(x): 0.8966 D(G(z)): 0.0905 / 0.1155 [315/1000][0/4] Loss_D: 0.1964 Loss_G: 3.8001 D(x): 0.9380 D(G(z)): 0.1164 / 0.0347 [316/1000][0/4] Loss_D: 0.2653 Loss_G: 3.3417 D(x): 0.9005 D(G(z)): 0.1362 / 0.0522 [317/1000][0/4] Loss_D: 0.3482 Loss_G: 4.6230 D(x): 0.9616 D(G(z)): 0.2473 / 0.0157 [318/1000][0/4] Loss_D: 0.1686 Loss_G: 3.6156 D(x): 0.9165 D(G(z)): 0.0736 / 0.0420 [319/1000][0/4] Loss_D: 0.2734 Loss_G: 3.8274 D(x): 0.9444 D(G(z)): 0.1819 / 0.0324 [320/1000][0/4] Loss_D: 0.2786 Loss_G: 2.0005 D(x): 0.8100 D(G(z)): 0.0566 / 0.1727 [321/1000][0/4] Loss_D: 0.2999 Loss_G: 4.6503 D(x): 0.9752 D(G(z)): 0.2209 / 0.0149 [322/1000][0/4] Loss_D: 0.1646 Loss_G: 3.5322 D(x): 0.9344 D(G(z)): 0.0879 / 0.0407 [323/1000][0/4] Loss_D: 0.1913 Loss_G: 3.5090 D(x): 0.9454 D(G(z)): 0.1211 / 0.0414 [324/1000][0/4] Loss_D: 0.1874 Loss_G: 3.4287 D(x): 0.9359 D(G(z)): 0.1092 / 0.0447 [325/1000][0/4] Loss_D: 0.1765 Loss_G: 3.1781 D(x): 0.9278 D(G(z)): 0.0928 / 0.0560 [326/1000][0/4] Loss_D: 0.2254 Loss_G: 3.7930 D(x): 0.9499 D(G(z)): 0.1496 / 0.0319 [327/1000][0/4] Loss_D: 0.1796 Loss_G: 3.2208 D(x): 0.9168 D(G(z)): 0.0843 / 0.0557 [328/1000][0/4] Loss_D: 0.1737 Loss_G: 3.2141 D(x): 0.9249 D(G(z)): 0.0877 / 0.0542 [329/1000][0/4] Loss_D: 0.5842 Loss_G: 1.0885 D(x): 0.5908 D(G(z)): 0.0078 / 0.3875 [330/1000][0/4] Loss_D: 5.8499 Loss_G: 0.9999 D(x): 0.0066 D(G(z)): 0.0004 / 0.4800 [331/1000][0/4] Loss_D: 1.6829 Loss_G: 0.9038 D(x): 0.2768 D(G(z)): 0.0177 / 0.5138 [332/1000][0/4] Loss_D: 0.8429 Loss_G: 1.8165 D(x): 0.5455 D(G(z)): 0.0990 / 0.2227 [333/1000][0/4] Loss_D: 0.4484 Loss_G: 3.2772 D(x): 0.7708 D(G(z)): 0.1398 / 0.0622 [334/1000][0/4] Loss_D: 0.7480 Loss_G: 5.1627 D(x): 0.9283 D(G(z)): 0.4456 / 0.0106 [335/1000][0/4] Loss_D: 0.4411 Loss_G: 2.0494 D(x): 0.7158 D(G(z)): 0.0694 / 0.1731 [336/1000][0/4] Loss_D: 0.3978 Loss_G: 3.3445 D(x): 0.9113 D(G(z)): 0.2425 / 0.0492 [337/1000][0/4] Loss_D: 0.3031 Loss_G: 2.9784 D(x): 0.8564 D(G(z)): 0.1261 / 0.0696 [338/1000][0/4] Loss_D: 0.3270 Loss_G: 3.3986 D(x): 0.9156 D(G(z)): 0.1963 / 0.0501 [339/1000][0/4] Loss_D: 0.3416 Loss_G: 4.0271 D(x): 0.9318 D(G(z)): 0.2189 / 0.0319 [340/1000][0/4] Loss_D: 0.3958 Loss_G: 3.8909 D(x): 0.9523 D(G(z)): 0.2711 / 0.0350 [341/1000][0/4] Loss_D: 0.2769 Loss_G: 2.9627 D(x): 0.8301 D(G(z)): 0.0749 / 0.0799 [342/1000][0/4] Loss_D: 0.2442 Loss_G: 3.1756 D(x): 0.9083 D(G(z)): 0.1294 / 0.0569 [343/1000][0/4] Loss_D: 0.3300 Loss_G: 4.0667 D(x): 0.9506 D(G(z)): 0.2268 / 0.0260 [344/1000][0/4] Loss_D: 0.2733 Loss_G: 2.4721 D(x): 0.8213 D(G(z)): 0.0637 / 0.1183 [345/1000][0/4] Loss_D: 0.2352 Loss_G: 2.7764 D(x): 0.8709 D(G(z)): 0.0860 / 0.0864 [346/1000][0/4] Loss_D: 0.2225 Loss_G: 3.3509 D(x): 0.9267 D(G(z)): 0.1295 / 0.0489 [347/1000][0/4] Loss_D: 0.2409 Loss_G: 3.6357 D(x): 0.9345 D(G(z)): 0.1511 / 0.0394 [348/1000][0/4] Loss_D: 0.2557 Loss_G: 4.0205 D(x): 0.9607 D(G(z)): 0.1833 / 0.0258 [349/1000][0/4] Loss_D: 0.2274 Loss_G: 2.6073 D(x): 0.8719 D(G(z)): 0.0793 / 0.0999 [350/1000][0/4] Loss_D: 0.2543 Loss_G: 4.0799 D(x): 0.9543 D(G(z)): 0.1759 / 0.0243 [351/1000][0/4] Loss_D: 0.5841 Loss_G: 4.7157 D(x): 0.9823 D(G(z)): 0.3933 / 0.0134 [352/1000][0/4] Loss_D: 0.2246 Loss_G: 3.1973 D(x): 0.8610 D(G(z)): 0.0642 / 0.0606 [353/1000][0/4] Loss_D: 0.2081 Loss_G: 2.9892 D(x): 0.9046 D(G(z)): 0.0966 / 0.0683 [354/1000][0/4] Loss_D: 0.2181 Loss_G: 2.7239 D(x): 0.8501 D(G(z)): 0.0484 / 0.0946 [355/1000][0/4] Loss_D: 0.2109 Loss_G: 3.1862 D(x): 0.9166 D(G(z)): 0.1100 / 0.0585 [356/1000][0/4] Loss_D: 0.1781 Loss_G: 3.3706 D(x): 0.9096 D(G(z)): 0.0763 / 0.0476 [357/1000][0/4] Loss_D: 0.3275 Loss_G: 4.1168 D(x): 0.9738 D(G(z)): 0.2428 / 0.0245 [358/1000][0/4] Loss_D: 0.2000 Loss_G: 3.2654 D(x): 0.9117 D(G(z)): 0.0973 / 0.0520 [359/1000][0/4] Loss_D: 0.2116 Loss_G: 2.7958 D(x): 0.8802 D(G(z)): 0.0745 / 0.0825 [360/1000][0/4] Loss_D: 0.2711 Loss_G: 2.3705 D(x): 0.8098 D(G(z)): 0.0480 / 0.1333 [361/1000][0/4] Loss_D: 0.1773 Loss_G: 3.4290 D(x): 0.9086 D(G(z)): 0.0738 / 0.0470 [362/1000][0/4] Loss_D: 0.2342 Loss_G: 4.3620 D(x): 0.9609 D(G(z)): 0.1650 / 0.0198 [363/1000][0/4] Loss_D: 0.1813 Loss_G: 2.9426 D(x): 0.9509 D(G(z)): 0.1161 / 0.0797 [364/1000][0/4] Loss_D: 0.2087 Loss_G: 3.9946 D(x): 0.9607 D(G(z)): 0.1443 / 0.0271 [365/1000][0/4] Loss_D: 0.1834 Loss_G: 3.3759 D(x): 0.9318 D(G(z)): 0.0991 / 0.0492 [366/1000][0/4] Loss_D: 0.2108 Loss_G: 2.7620 D(x): 0.8884 D(G(z)): 0.0823 / 0.0875 [367/1000][0/4] Loss_D: 0.1754 Loss_G: 3.4108 D(x): 0.9185 D(G(z)): 0.0825 / 0.0467 [368/1000][0/4] Loss_D: 0.2042 Loss_G: 2.3664 D(x): 0.8600 D(G(z)): 0.0468 / 0.1297 [369/1000][0/4] Loss_D: 0.1625 Loss_G: 3.2913 D(x): 0.9131 D(G(z)): 0.0661 / 0.0514 [370/1000][0/4] Loss_D: 0.1467 Loss_G: 3.6162 D(x): 0.9322 D(G(z)): 0.0704 / 0.0400 [371/1000][0/4] Loss_D: 0.5585 Loss_G: 6.4963 D(x): 0.9860 D(G(z)): 0.3885 / 0.0027 [372/1000][0/4] Loss_D: 2.0842 Loss_G: 10.8389 D(x): 0.9967 D(G(z)): 0.8053 / 0.0002 [373/1000][0/4] Loss_D: 2.1511 Loss_G: 5.4795 D(x): 0.9812 D(G(z)): 0.7713 / 0.0137 [374/1000][0/4] Loss_D: 0.6372 Loss_G: 2.2474 D(x): 0.6956 D(G(z)): 0.1778 / 0.1506 [375/1000][0/4] Loss_D: 0.4710 Loss_G: 2.5356 D(x): 0.7788 D(G(z)): 0.1648 / 0.1068 [376/1000][0/4] Loss_D: 0.7690 Loss_G: 1.5542 D(x): 0.5489 D(G(z)): 0.0556 / 0.2914 [377/1000][0/4] Loss_D: 0.3451 Loss_G: 3.2431 D(x): 0.8730 D(G(z)): 0.1710 / 0.0609 [378/1000][0/4] Loss_D: 0.2866 Loss_G: 3.1279 D(x): 0.8960 D(G(z)): 0.1495 / 0.0634 [379/1000][0/4] Loss_D: 0.2827 Loss_G: 3.0150 D(x): 0.8586 D(G(z)): 0.1113 / 0.0676 [380/1000][0/4] Loss_D: 0.3477 Loss_G: 3.0841 D(x): 0.9009 D(G(z)): 0.2001 / 0.0680 [381/1000][0/4] Loss_D: 0.2700 Loss_G: 3.3691 D(x): 0.9033 D(G(z)): 0.1459 / 0.0482 [382/1000][0/4] Loss_D: 0.2571 Loss_G: 3.5369 D(x): 0.9340 D(G(z)): 0.1611 / 0.0441 [383/1000][0/4] Loss_D: 0.2813 Loss_G: 3.9981 D(x): 0.9538 D(G(z)): 0.1954 / 0.0292 [384/1000][0/4] Loss_D: 0.2185 Loss_G: 2.7782 D(x): 0.8883 D(G(z)): 0.0880 / 0.0857 [385/1000][0/4] Loss_D: 0.2414 Loss_G: 3.3066 D(x): 0.9210 D(G(z)): 0.1388 / 0.0526 [386/1000][0/4] Loss_D: 0.2227 Loss_G: 2.5815 D(x): 0.8546 D(G(z)): 0.0579 / 0.1004 [387/1000][0/4] Loss_D: 0.2410 Loss_G: 3.6740 D(x): 0.9332 D(G(z)): 0.1481 / 0.0372 [388/1000][0/4] Loss_D: 0.2366 Loss_G: 2.6065 D(x): 0.8417 D(G(z)): 0.0548 / 0.1026 [389/1000][0/4] Loss_D: 0.2796 Loss_G: 3.7826 D(x): 0.9459 D(G(z)): 0.1882 / 0.0346 [390/1000][0/4] Loss_D: 0.2267 Loss_G: 3.3220 D(x): 0.9388 D(G(z)): 0.1426 / 0.0513 [391/1000][0/4] Loss_D: 0.1841 Loss_G: 3.1949 D(x): 0.9183 D(G(z)): 0.0897 / 0.0578 [392/1000][0/4] Loss_D: 0.2007 Loss_G: 3.3071 D(x): 0.9107 D(G(z)): 0.0962 / 0.0512 [393/1000][0/4] Loss_D: 0.2076 Loss_G: 3.2687 D(x): 0.9078 D(G(z)): 0.0991 / 0.0560 [394/1000][0/4] Loss_D: 0.1854 Loss_G: 3.2163 D(x): 0.9347 D(G(z)): 0.1059 / 0.0550 [395/1000][0/4] Loss_D: 0.2012 Loss_G: 3.6380 D(x): 0.9357 D(G(z)): 0.1191 / 0.0366 [396/1000][0/4] Loss_D: 0.1762 Loss_G: 3.5999 D(x): 0.9510 D(G(z)): 0.1122 / 0.0379 [397/1000][0/4] Loss_D: 0.1765 Loss_G: 3.2151 D(x): 0.9255 D(G(z)): 0.0908 / 0.0536 [398/1000][0/4] Loss_D: 0.1897 Loss_G: 3.8004 D(x): 0.9597 D(G(z)): 0.1314 / 0.0316 [399/1000][0/4] Loss_D: 0.2170 Loss_G: 4.0429 D(x): 0.9659 D(G(z)): 0.1574 / 0.0240 [400/1000][0/4] Loss_D: 0.1819 Loss_G: 3.0555 D(x): 0.9136 D(G(z)): 0.0838 / 0.0638 [401/1000][0/4] Loss_D: 0.1523 Loss_G: 3.4006 D(x): 0.9262 D(G(z)): 0.0701 / 0.0472 [402/1000][0/4] Loss_D: 0.5784 Loss_G: 6.3977 D(x): 0.9886 D(G(z)): 0.3928 / 0.0035 [403/1000][0/4] Loss_D: 2.8264 Loss_G: 9.9560 D(x): 0.9989 D(G(z)): 0.8738 / 0.0003 [404/1000][0/4] Loss_D: 1.1764 Loss_G: 4.3006 D(x): 0.9412 D(G(z)): 0.5700 / 0.0371 [405/1000][0/4] Loss_D: 0.5634 Loss_G: 2.5757 D(x): 0.7062 D(G(z)): 0.1402 / 0.1124 [406/1000][0/4] Loss_D: 0.4485 Loss_G: 3.2336 D(x): 0.8449 D(G(z)): 0.2185 / 0.0631 [407/1000][0/4] Loss_D: 0.3118 Loss_G: 3.2698 D(x): 0.8684 D(G(z)): 0.1422 / 0.0591 [408/1000][0/4] Loss_D: 0.3192 Loss_G: 3.3781 D(x): 0.8543 D(G(z)): 0.1318 / 0.0527 [409/1000][0/4] Loss_D: 0.2484 Loss_G: 3.3983 D(x): 0.8993 D(G(z)): 0.1231 / 0.0478 [410/1000][0/4] Loss_D: 0.2312 Loss_G: 2.9570 D(x): 0.8822 D(G(z)): 0.0924 / 0.0715 [411/1000][0/4] Loss_D: 0.2061 Loss_G: 3.0612 D(x): 0.8960 D(G(z)): 0.0857 / 0.0648 [412/1000][0/4] Loss_D: 0.2435 Loss_G: 3.7733 D(x): 0.9296 D(G(z)): 0.1490 / 0.0321 [413/1000][0/4] Loss_D: 0.1874 Loss_G: 3.1911 D(x): 0.9058 D(G(z)): 0.0802 / 0.0571 [414/1000][0/4] Loss_D: 0.2342 Loss_G: 3.5584 D(x): 0.9443 D(G(z)): 0.1531 / 0.0410 [415/1000][0/4] Loss_D: 0.1910 Loss_G: 3.2233 D(x): 0.8820 D(G(z)): 0.0586 / 0.0606 [416/1000][0/4] Loss_D: 0.1922 Loss_G: 3.0925 D(x): 0.9088 D(G(z)): 0.0874 / 0.0651 [417/1000][0/4] Loss_D: 0.1735 Loss_G: 3.2496 D(x): 0.9277 D(G(z)): 0.0898 / 0.0532 [418/1000][0/4] Loss_D: 0.1976 Loss_G: 3.2077 D(x): 0.9115 D(G(z)): 0.0944 / 0.0569 [419/1000][0/4] Loss_D: 0.5399 Loss_G: 5.3870 D(x): 0.9858 D(G(z)): 0.3759 / 0.0067 [420/1000][0/4] Loss_D: 0.1625 Loss_G: 3.6314 D(x): 0.8970 D(G(z)): 0.0467 / 0.0366 [421/1000][0/4] Loss_D: 0.1936 Loss_G: 3.2854 D(x): 0.9220 D(G(z)): 0.1006 / 0.0542 [422/1000][0/4] Loss_D: 0.1638 Loss_G: 3.4574 D(x): 0.9356 D(G(z)): 0.0890 / 0.0451 [423/1000][0/4] Loss_D: 0.1690 Loss_G: 3.3514 D(x): 0.9169 D(G(z)): 0.0753 / 0.0512 [424/1000][0/4] Loss_D: 0.2537 Loss_G: 4.2904 D(x): 0.9672 D(G(z)): 0.1848 / 0.0198 [425/1000][0/4] Loss_D: 0.1663 Loss_G: 3.2441 D(x): 0.9061 D(G(z)): 0.0601 / 0.0562 [426/1000][0/4] Loss_D: 0.1624 Loss_G: 3.4036 D(x): 0.9391 D(G(z)): 0.0901 / 0.0481 [427/1000][0/4] Loss_D: 0.1431 Loss_G: 3.5336 D(x): 0.9216 D(G(z)): 0.0573 / 0.0417 [428/1000][0/4] Loss_D: 0.1622 Loss_G: 3.1974 D(x): 0.9061 D(G(z)): 0.0576 / 0.0576 [429/1000][0/4] Loss_D: 0.1577 Loss_G: 3.5477 D(x): 0.9546 D(G(z)): 0.1005 / 0.0408 [430/1000][0/4] Loss_D: 0.1445 Loss_G: 3.5899 D(x): 0.9397 D(G(z)): 0.0754 / 0.0412 [431/1000][0/4] Loss_D: 0.1577 Loss_G: 2.8801 D(x): 0.9123 D(G(z)): 0.0608 / 0.0783 [432/1000][0/4] Loss_D: 0.1448 Loss_G: 3.1847 D(x): 0.9363 D(G(z)): 0.0733 / 0.0555 [433/1000][0/4] Loss_D: 0.1471 Loss_G: 3.3530 D(x): 0.9148 D(G(z)): 0.0527 / 0.0505 [434/1000][0/4] Loss_D: 0.1385 Loss_G: 3.2150 D(x): 0.9265 D(G(z)): 0.0571 / 0.0572 [435/1000][0/4] Loss_D: 0.1521 Loss_G: 3.1429 D(x): 0.9174 D(G(z)): 0.0608 / 0.0610 [436/1000][0/4] Loss_D: 0.1527 Loss_G: 3.1535 D(x): 0.9108 D(G(z)): 0.0540 / 0.0620 [437/1000][0/4] Loss_D: 0.1420 Loss_G: 3.5860 D(x): 0.9475 D(G(z)): 0.0810 / 0.0383 [438/1000][0/4] Loss_D: 0.1089 Loss_G: 3.4164 D(x): 0.9373 D(G(z)): 0.0416 / 0.0465 [439/1000][0/4] Loss_D: 0.2388 Loss_G: 2.2203 D(x): 0.8152 D(G(z)): 0.0240 / 0.1453 [440/1000][0/4] Loss_D: 0.1564 Loss_G: 3.9140 D(x): 0.9519 D(G(z)): 0.0950 / 0.0316 [441/1000][0/4] Loss_D: 0.1351 Loss_G: 3.5920 D(x): 0.9164 D(G(z)): 0.0434 / 0.0416 [442/1000][0/4] Loss_D: 0.1638 Loss_G: 3.4322 D(x): 0.9286 D(G(z)): 0.0817 / 0.0472 [443/1000][0/4] Loss_D: 0.1467 Loss_G: 3.4515 D(x): 0.9426 D(G(z)): 0.0800 / 0.0484 [444/1000][0/4] Loss_D: 0.1436 Loss_G: 3.6643 D(x): 0.9540 D(G(z)): 0.0892 / 0.0350 [445/1000][0/4] Loss_D: 0.1356 Loss_G: 3.0913 D(x): 0.9270 D(G(z)): 0.0558 / 0.0631 [446/1000][0/4] Loss_D: 0.1496 Loss_G: 3.9161 D(x): 0.9588 D(G(z)): 0.0966 / 0.0289 [447/1000][0/4] Loss_D: 0.1220 Loss_G: 3.2804 D(x): 0.9383 D(G(z)): 0.0543 / 0.0561 [448/1000][0/4] Loss_D: 0.4421 Loss_G: 6.4666 D(x): 0.9904 D(G(z)): 0.3182 / 0.0025 [449/1000][0/4] Loss_D: 3.5845 Loss_G: 8.1937 D(x): 0.9980 D(G(z)): 0.8892 / 0.0014 [450/1000][0/4] Loss_D: 0.6295 Loss_G: 2.5324 D(x): 0.8177 D(G(z)): 0.2800 / 0.1273 [451/1000][0/4] Loss_D: 0.5433 Loss_G: 2.0046 D(x): 0.7201 D(G(z)): 0.1386 / 0.1950 [452/1000][0/4] Loss_D: 0.4322 Loss_G: 3.0124 D(x): 0.7364 D(G(z)): 0.0761 / 0.0792 [453/1000][0/4] Loss_D: 0.4267 Loss_G: 3.8957 D(x): 0.9253 D(G(z)): 0.2654 / 0.0302 [454/1000][0/4] Loss_D: 0.2772 Loss_G: 2.6331 D(x): 0.8284 D(G(z)): 0.0728 / 0.0997 [455/1000][0/4] Loss_D: 0.2136 Loss_G: 3.2024 D(x): 0.8967 D(G(z)): 0.0929 / 0.0588 [456/1000][0/4] Loss_D: 0.2139 Loss_G: 3.1566 D(x): 0.8856 D(G(z)): 0.0814 / 0.0637 [457/1000][0/4] Loss_D: 0.2049 Loss_G: 3.6480 D(x): 0.9412 D(G(z)): 0.1264 / 0.0380 [458/1000][0/4] Loss_D: 0.1750 Loss_G: 3.6321 D(x): 0.9466 D(G(z)): 0.1068 / 0.0382 [459/1000][0/4] Loss_D: 0.1594 Loss_G: 3.5494 D(x): 0.9410 D(G(z)): 0.0893 / 0.0428 [460/1000][0/4] Loss_D: 0.2728 Loss_G: 4.0335 D(x): 0.9552 D(G(z)): 0.1882 / 0.0269 [461/1000][0/4] Loss_D: 0.1722 Loss_G: 3.6274 D(x): 0.9305 D(G(z)): 0.0900 / 0.0391 [462/1000][0/4] Loss_D: 0.1785 Loss_G: 3.6446 D(x): 0.9462 D(G(z)): 0.1100 / 0.0375 [463/1000][0/4] Loss_D: 0.1620 Loss_G: 3.3995 D(x): 0.9241 D(G(z)): 0.0757 / 0.0477 [464/1000][0/4] Loss_D: 0.1452 Loss_G: 3.2313 D(x): 0.9254 D(G(z)): 0.0627 / 0.0565 [465/1000][0/4] Loss_D: 0.2563 Loss_G: 4.3656 D(x): 0.9708 D(G(z)): 0.1886 / 0.0202 [466/1000][0/4] Loss_D: 0.1487 Loss_G: 3.7191 D(x): 0.9532 D(G(z)): 0.0912 / 0.0351 [467/1000][0/4] Loss_D: 0.1653 Loss_G: 3.4402 D(x): 0.9436 D(G(z)): 0.0954 / 0.0460 [468/1000][0/4] Loss_D: 0.1411 Loss_G: 3.4009 D(x): 0.9250 D(G(z)): 0.0582 / 0.0495 [469/1000][0/4] Loss_D: 0.1755 Loss_G: 3.0389 D(x): 0.8739 D(G(z)): 0.0365 / 0.0700 [470/1000][0/4] Loss_D: 0.1398 Loss_G: 3.0891 D(x): 0.9307 D(G(z)): 0.0630 / 0.0659 [471/1000][0/4] Loss_D: 0.1585 Loss_G: 3.3351 D(x): 0.8826 D(G(z)): 0.0301 / 0.0524 [472/1000][0/4] Loss_D: 0.1637 Loss_G: 3.8067 D(x): 0.9711 D(G(z)): 0.1208 / 0.0319 [473/1000][0/4] Loss_D: 0.1411 Loss_G: 3.3103 D(x): 0.9343 D(G(z)): 0.0669 / 0.0534 [474/1000][0/4] Loss_D: 0.1518 Loss_G: 3.6733 D(x): 0.9608 D(G(z)): 0.1018 / 0.0352 [475/1000][0/4] Loss_D: 0.1447 Loss_G: 3.4204 D(x): 0.9277 D(G(z)): 0.0647 / 0.0483 [476/1000][0/4] Loss_D: 0.1141 Loss_G: 3.3526 D(x): 0.9294 D(G(z)): 0.0385 / 0.0483 [477/1000][0/4] Loss_D: 0.1306 Loss_G: 3.7866 D(x): 0.9528 D(G(z)): 0.0753 / 0.0329 [478/1000][0/4] Loss_D: 0.1473 Loss_G: 3.7879 D(x): 0.9614 D(G(z)): 0.0971 / 0.0323 [479/1000][0/4] Loss_D: 0.1166 Loss_G: 3.3846 D(x): 0.9332 D(G(z)): 0.0448 / 0.0499 [480/1000][0/4] Loss_D: 0.1476 Loss_G: 3.2074 D(x): 0.9006 D(G(z)): 0.0387 / 0.0614 [481/1000][0/4] Loss_D: 0.1316 Loss_G: 3.3710 D(x): 0.9399 D(G(z)): 0.0639 / 0.0510 [482/1000][0/4] Loss_D: 0.1791 Loss_G: 2.1522 D(x): 0.8733 D(G(z)): 0.0379 / 0.1554 [483/1000][0/4] Loss_D: 0.1079 Loss_G: 3.9369 D(x): 0.9580 D(G(z)): 0.0609 / 0.0286 [484/1000][0/4] Loss_D: 0.1046 Loss_G: 3.6031 D(x): 0.9503 D(G(z)): 0.0502 / 0.0408 [485/1000][0/4] Loss_D: 0.1029 Loss_G: 3.5570 D(x): 0.9545 D(G(z)): 0.0532 / 0.0395 [486/1000][0/4] Loss_D: 0.1107 Loss_G: 3.6425 D(x): 0.9549 D(G(z)): 0.0603 / 0.0379 [487/1000][0/4] Loss_D: 0.1064 Loss_G: 3.7927 D(x): 0.9577 D(G(z)): 0.0596 / 0.0314 [488/1000][0/4] Loss_D: 0.1177 Loss_G: 3.6014 D(x): 0.9496 D(G(z)): 0.0609 / 0.0387 [489/1000][0/4] Loss_D: 0.1056 Loss_G: 3.7400 D(x): 0.9389 D(G(z)): 0.0402 / 0.0342 [490/1000][0/4] Loss_D: 0.1424 Loss_G: 3.9625 D(x): 0.9686 D(G(z)): 0.0988 / 0.0282 [491/1000][0/4] Loss_D: 0.1000 Loss_G: 3.6759 D(x): 0.9601 D(G(z)): 0.0558 / 0.0363 [492/1000][0/4] Loss_D: 0.1069 Loss_G: 3.7634 D(x): 0.9538 D(G(z)): 0.0562 / 0.0328 [493/1000][0/4] Loss_D: 0.1178 Loss_G: 4.3304 D(x): 0.9727 D(G(z)): 0.0827 / 0.0192 [494/1000][0/4] Loss_D: 0.1205 Loss_G: 4.0919 D(x): 0.9659 D(G(z)): 0.0795 / 0.0238 [495/1000][0/4] Loss_D: 0.1055 Loss_G: 3.6405 D(x): 0.9527 D(G(z)): 0.0536 / 0.0389 [496/1000][0/4] Loss_D: 0.1019 Loss_G: 3.7558 D(x): 0.9526 D(G(z)): 0.0504 / 0.0345 [497/1000][0/4] Loss_D: 0.0949 Loss_G: 3.9690 D(x): 0.9657 D(G(z)): 0.0565 / 0.0270 [498/1000][0/4] Loss_D: 0.2500 Loss_G: 5.7996 D(x): 0.9891 D(G(z)): 0.1951 / 0.0046 [499/1000][0/4] Loss_D: 2.3112 Loss_G: 12.8323 D(x): 0.9978 D(G(z)): 0.7993 / 0.0000 [500/1000][0/4] Loss_D: 2.4066 Loss_G: 3.9579 D(x): 0.9754 D(G(z)): 0.8157 / 0.0496 [501/1000][0/4] Loss_D: 0.7282 Loss_G: 3.6775 D(x): 0.8549 D(G(z)): 0.3863 / 0.0423 [502/1000][0/4] Loss_D: 0.7969 Loss_G: 4.1598 D(x): 0.9254 D(G(z)): 0.4409 / 0.0277 [503/1000][0/4] Loss_D: 0.4018 Loss_G: 2.8602 D(x): 0.8197 D(G(z)): 0.1553 / 0.0838 [504/1000][0/4] Loss_D: 0.3114 Loss_G: 3.3877 D(x): 0.8993 D(G(z)): 0.1698 / 0.0516 [505/1000][0/4] Loss_D: 0.2976 Loss_G: 3.2104 D(x): 0.8771 D(G(z)): 0.1385 / 0.0597 [506/1000][0/4] Loss_D: 0.2653 Loss_G: 3.4772 D(x): 0.8833 D(G(z)): 0.1162 / 0.0547 [507/1000][0/4] Loss_D: 0.2150 Loss_G: 3.4933 D(x): 0.9281 D(G(z)): 0.1228 / 0.0449 [508/1000][0/4] Loss_D: 0.2149 Loss_G: 3.0380 D(x): 0.8883 D(G(z)): 0.0846 / 0.0714 [509/1000][0/4] Loss_D: 0.2138 Loss_G: 2.9628 D(x): 0.8905 D(G(z)): 0.0865 / 0.0747 [510/1000][0/4] Loss_D: 0.1833 Loss_G: 3.3355 D(x): 0.9205 D(G(z)): 0.0895 / 0.0479 [511/1000][0/4] Loss_D: 0.2272 Loss_G: 4.0937 D(x): 0.9563 D(G(z)): 0.1565 / 0.0240 [512/1000][0/4] Loss_D: 0.1500 Loss_G: 3.6305 D(x): 0.9221 D(G(z)): 0.0629 / 0.0411 [513/1000][0/4] Loss_D: 0.1565 Loss_G: 3.4406 D(x): 0.9368 D(G(z)): 0.0835 / 0.0468 [514/1000][0/4] Loss_D: 0.1664 Loss_G: 3.6256 D(x): 0.9503 D(G(z)): 0.1052 / 0.0376 [515/1000][0/4] Loss_D: 0.1715 Loss_G: 3.0269 D(x): 0.8795 D(G(z)): 0.0377 / 0.0727 [516/1000][0/4] Loss_D: 0.2189 Loss_G: 4.0772 D(x): 0.9627 D(G(z)): 0.1538 / 0.0256 [517/1000][0/4] Loss_D: 0.1714 Loss_G: 3.1573 D(x): 0.8862 D(G(z)): 0.0445 / 0.0619 [518/1000][0/4] Loss_D: 0.1729 Loss_G: 3.6668 D(x): 0.9624 D(G(z)): 0.1206 / 0.0362 [519/1000][0/4] Loss_D: 0.1314 Loss_G: 3.4095 D(x): 0.9343 D(G(z)): 0.0595 / 0.0452 [520/1000][0/4] Loss_D: 0.1539 Loss_G: 2.9602 D(x): 0.8894 D(G(z)): 0.0328 / 0.0733 [521/1000][0/4] Loss_D: 0.1142 Loss_G: 3.2299 D(x): 0.9318 D(G(z)): 0.0412 / 0.0557 [522/1000][0/4] Loss_D: 0.1427 Loss_G: 3.7608 D(x): 0.9675 D(G(z)): 0.0993 / 0.0329 [523/1000][0/4] Loss_D: 0.1141 Loss_G: 3.5824 D(x): 0.9428 D(G(z)): 0.0521 / 0.0377 [524/1000][0/4] Loss_D: 0.1086 Loss_G: 3.3249 D(x): 0.9375 D(G(z)): 0.0418 / 0.0512 [525/1000][0/4] Loss_D: 0.1396 Loss_G: 4.0861 D(x): 0.9727 D(G(z)): 0.1015 / 0.0233 [526/1000][0/4] Loss_D: 0.1077 Loss_G: 4.0103 D(x): 0.9528 D(G(z)): 0.0555 / 0.0268 [527/1000][0/4] Loss_D: 0.1139 Loss_G: 3.2017 D(x): 0.9370 D(G(z)): 0.0461 / 0.0571 [528/1000][0/4] Loss_D: 0.1025 Loss_G: 3.5140 D(x): 0.9317 D(G(z)): 0.0300 / 0.0433 [529/1000][0/4] Loss_D: 0.1350 Loss_G: 4.0127 D(x): 0.9751 D(G(z)): 0.0999 / 0.0248 [530/1000][0/4] Loss_D: 0.1273 Loss_G: 4.0840 D(x): 0.9687 D(G(z)): 0.0871 / 0.0243 [531/1000][0/4] Loss_D: 0.1210 Loss_G: 3.5228 D(x): 0.9425 D(G(z)): 0.0570 / 0.0442 [532/1000][0/4] Loss_D: 0.1247 Loss_G: 3.7006 D(x): 0.9684 D(G(z)): 0.0857 / 0.0342 [533/1000][0/4] Loss_D: 0.1079 Loss_G: 3.3960 D(x): 0.9364 D(G(z)): 0.0395 / 0.0462 [534/1000][0/4] Loss_D: 0.1069 Loss_G: 3.5931 D(x): 0.9581 D(G(z)): 0.0601 / 0.0385 [535/1000][0/4] Loss_D: 0.1056 Loss_G: 3.6947 D(x): 0.9568 D(G(z)): 0.0570 / 0.0346 [536/1000][0/4] Loss_D: 0.1723 Loss_G: 5.0902 D(x): 0.9879 D(G(z)): 0.1392 / 0.0089 [537/1000][0/4] Loss_D: 0.0949 Loss_G: 3.6986 D(x): 0.9415 D(G(z)): 0.0328 / 0.0366 [538/1000][0/4] Loss_D: 0.1263 Loss_G: 4.0841 D(x): 0.9767 D(G(z)): 0.0932 / 0.0247 [539/1000][0/4] Loss_D: 0.0983 Loss_G: 4.0797 D(x): 0.9672 D(G(z)): 0.0604 / 0.0263 [540/1000][0/4] Loss_D: 0.1017 Loss_G: 3.6994 D(x): 0.9623 D(G(z)): 0.0593 / 0.0361 [541/1000][0/4] Loss_D: 0.0858 Loss_G: 4.0671 D(x): 0.9691 D(G(z)): 0.0515 / 0.0250 [542/1000][0/4] Loss_D: 0.0967 Loss_G: 4.4529 D(x): 0.9828 D(G(z)): 0.0740 / 0.0166 [543/1000][0/4] Loss_D: 0.0940 Loss_G: 3.8895 D(x): 0.9683 D(G(z)): 0.0582 / 0.0300 [544/1000][0/4] Loss_D: 0.0985 Loss_G: 3.4727 D(x): 0.9473 D(G(z)): 0.0422 / 0.0428 [545/1000][0/4] Loss_D: 0.0836 Loss_G: 3.8911 D(x): 0.9646 D(G(z)): 0.0453 / 0.0291 [546/1000][0/4] Loss_D: 0.1203 Loss_G: 2.7334 D(x): 0.9079 D(G(z)): 0.0221 / 0.0896 [547/1000][0/4] Loss_D: 0.0926 Loss_G: 3.8551 D(x): 0.9749 D(G(z)): 0.0629 / 0.0298 [548/1000][0/4] Loss_D: 0.0803 Loss_G: 3.7342 D(x): 0.9523 D(G(z)): 0.0301 / 0.0334 [549/1000][0/4] Loss_D: 0.0795 Loss_G: 3.8182 D(x): 0.9660 D(G(z)): 0.0430 / 0.0316 [550/1000][0/4] Loss_D: 0.0860 Loss_G: 4.0332 D(x): 0.9746 D(G(z)): 0.0566 / 0.0257 [551/1000][0/4] Loss_D: 0.0834 Loss_G: 3.9555 D(x): 0.9697 D(G(z)): 0.0501 / 0.0263 [552/1000][0/4] Loss_D: 0.0839 Loss_G: 4.0074 D(x): 0.9681 D(G(z)): 0.0490 / 0.0259 [553/1000][0/4] Loss_D: 0.0935 Loss_G: 3.9271 D(x): 0.9609 D(G(z)): 0.0507 / 0.0288 [554/1000][0/4] Loss_D: 0.0836 Loss_G: 3.9646 D(x): 0.9693 D(G(z)): 0.0500 / 0.0265 [555/1000][0/4] Loss_D: 0.1121 Loss_G: 4.7950 D(x): 0.9834 D(G(z)): 0.0875 / 0.0119 [556/1000][0/4] Loss_D: 0.0774 Loss_G: 3.7767 D(x): 0.9520 D(G(z)): 0.0266 / 0.0350 [557/1000][0/4] Loss_D: 0.0803 Loss_G: 3.8075 D(x): 0.9665 D(G(z)): 0.0442 / 0.0312 [558/1000][0/4] Loss_D: 0.0847 Loss_G: 3.6067 D(x): 0.9491 D(G(z)): 0.0306 / 0.0395 [559/1000][0/4] Loss_D: 0.1064 Loss_G: 4.6054 D(x): 0.9841 D(G(z)): 0.0836 / 0.0139 [560/1000][0/4] Loss_D: 0.0801 Loss_G: 3.7207 D(x): 0.9477 D(G(z)): 0.0249 / 0.0355 [561/1000][0/4] Loss_D: 0.0681 Loss_G: 3.8686 D(x): 0.9671 D(G(z)): 0.0333 / 0.0295 [562/1000][0/4] Loss_D: 0.0917 Loss_G: 4.2415 D(x): 0.9649 D(G(z)): 0.0525 / 0.0219 [563/1000][0/4] Loss_D: 0.0867 Loss_G: 4.4279 D(x): 0.9768 D(G(z)): 0.0594 / 0.0171 [564/1000][0/4] Loss_D: 0.0606 Loss_G: 4.2244 D(x): 0.9736 D(G(z)): 0.0327 / 0.0207 [565/1000][0/4] Loss_D: 0.0764 Loss_G: 3.6625 D(x): 0.9454 D(G(z)): 0.0187 / 0.0379 [566/1000][0/4] Loss_D: 0.0733 Loss_G: 4.0129 D(x): 0.9684 D(G(z)): 0.0395 / 0.0258 [567/1000][0/4] Loss_D: 0.1020 Loss_G: 3.2566 D(x): 0.9171 D(G(z)): 0.0143 / 0.0553 [568/1000][0/4] Loss_D: 0.1388 Loss_G: 4.9622 D(x): 0.9857 D(G(z)): 0.1113 / 0.0095 [569/1000][0/4] Loss_D: 0.1134 Loss_G: 3.2089 D(x): 0.9108 D(G(z)): 0.0176 / 0.0643 [570/1000][0/4] Loss_D: 0.0818 Loss_G: 3.9329 D(x): 0.9713 D(G(z)): 0.0500 / 0.0284 [571/1000][0/4] Loss_D: 0.0643 Loss_G: 4.0497 D(x): 0.9660 D(G(z)): 0.0286 / 0.0270 [572/1000][0/4] Loss_D: 0.1134 Loss_G: 5.1584 D(x): 0.9887 D(G(z)): 0.0933 / 0.0086 [573/1000][0/4] Loss_D: 0.0781 Loss_G: 4.2457 D(x): 0.9840 D(G(z)): 0.0581 / 0.0207 [574/1000][0/4] Loss_D: 0.0674 Loss_G: 4.0909 D(x): 0.9620 D(G(z)): 0.0274 / 0.0255 [575/1000][0/4] Loss_D: 0.1224 Loss_G: 4.8760 D(x): 0.9864 D(G(z)): 0.0973 / 0.0113 [576/1000][0/4] Loss_D: 0.0904 Loss_G: 4.4071 D(x): 0.9814 D(G(z)): 0.0668 / 0.0180 [577/1000][0/4] Loss_D: 0.0567 Loss_G: 4.2795 D(x): 0.9805 D(G(z)): 0.0356 / 0.0193 [578/1000][0/4] Loss_D: 0.0694 Loss_G: 4.1845 D(x): 0.9692 D(G(z)): 0.0361 / 0.0228 [579/1000][0/4] Loss_D: 0.0682 Loss_G: 4.2626 D(x): 0.9727 D(G(z)): 0.0388 / 0.0204 [580/1000][0/4] Loss_D: 0.0911 Loss_G: 4.1454 D(x): 0.9737 D(G(z)): 0.0604 / 0.0233 [581/1000][0/4] Loss_D: 0.0539 Loss_G: 4.1521 D(x): 0.9702 D(G(z)): 0.0230 / 0.0254 [582/1000][0/4] Loss_D: 0.0509 Loss_G: 4.5053 D(x): 0.9793 D(G(z)): 0.0290 / 0.0169 [583/1000][0/4] Loss_D: 0.0804 Loss_G: 4.2841 D(x): 0.9562 D(G(z)): 0.0339 / 0.0220 [584/1000][0/4] Loss_D: 0.0862 Loss_G: 3.3346 D(x): 0.9415 D(G(z)): 0.0247 / 0.0535 [585/1000][0/4] Loss_D: 0.0673 Loss_G: 4.3900 D(x): 0.9749 D(G(z)): 0.0399 / 0.0188 [586/1000][0/4] Loss_D: 0.0892 Loss_G: 3.1442 D(x): 0.9284 D(G(z)): 0.0137 / 0.0614 [587/1000][0/4] Loss_D: 0.0707 Loss_G: 4.3638 D(x): 0.9752 D(G(z)): 0.0433 / 0.0201 [588/1000][0/4] Loss_D: 0.0668 Loss_G: 3.9185 D(x): 0.9673 D(G(z)): 0.0325 / 0.0279 [589/1000][0/4] Loss_D: 0.0658 Loss_G: 3.9041 D(x): 0.9580 D(G(z)): 0.0220 / 0.0319 [590/1000][0/4] Loss_D: 0.0726 Loss_G: 4.3509 D(x): 0.9862 D(G(z)): 0.0554 / 0.0179 [591/1000][0/4] Loss_D: 0.0801 Loss_G: 4.8759 D(x): 0.9838 D(G(z)): 0.0600 / 0.0119 [592/1000][0/4] Loss_D: 0.0949 Loss_G: 4.5951 D(x): 0.9828 D(G(z)): 0.0722 / 0.0149 [593/1000][0/4] Loss_D: 0.1412 Loss_G: 2.6833 D(x): 0.8793 D(G(z)): 0.0098 / 0.1092 [594/1000][0/4] Loss_D: 0.0859 Loss_G: 4.3291 D(x): 0.9380 D(G(z)): 0.0202 / 0.0210 [595/1000][0/4] Loss_D: 0.0648 Loss_G: 3.8516 D(x): 0.9631 D(G(z)): 0.0264 / 0.0310 [596/1000][0/4] Loss_D: 0.0719 Loss_G: 3.6020 D(x): 0.9599 D(G(z)): 0.0296 / 0.0422 [597/1000][0/4] Loss_D: 0.1108 Loss_G: 4.6247 D(x): 0.9905 D(G(z)): 0.0930 / 0.0135 [598/1000][0/4] Loss_D: 0.0555 Loss_G: 4.4186 D(x): 0.9727 D(G(z)): 0.0269 / 0.0186 [599/1000][0/4] Loss_D: 0.0830 Loss_G: 4.7939 D(x): 0.9879 D(G(z)): 0.0665 / 0.0126 [600/1000][0/4] Loss_D: 0.0602 Loss_G: 4.3365 D(x): 0.9686 D(G(z)): 0.0274 / 0.0197 [601/1000][0/4] Loss_D: 0.0530 Loss_G: 4.2122 D(x): 0.9754 D(G(z)): 0.0273 / 0.0218 [602/1000][0/4] Loss_D: 0.0679 Loss_G: 3.7030 D(x): 0.9565 D(G(z)): 0.0226 / 0.0393 [603/1000][0/4] Loss_D: 0.0641 Loss_G: 4.2812 D(x): 0.9800 D(G(z)): 0.0413 / 0.0203 [604/1000][0/4] Loss_D: 0.0591 Loss_G: 4.8602 D(x): 0.9794 D(G(z)): 0.0365 / 0.0122 [605/1000][0/4] Loss_D: 0.0893 Loss_G: 4.0765 D(x): 0.9807 D(G(z)): 0.0626 / 0.0256 [606/1000][0/4] Loss_D: 0.0775 Loss_G: 4.6088 D(x): 0.9904 D(G(z)): 0.0639 / 0.0139 [607/1000][0/4] Loss_D: 0.0748 Loss_G: 4.7156 D(x): 0.9799 D(G(z)): 0.0515 / 0.0140 [608/1000][0/4] Loss_D: 0.0935 Loss_G: 5.0876 D(x): 0.9878 D(G(z)): 0.0755 / 0.0088 [609/1000][0/4] Loss_D: 0.0776 Loss_G: 4.7982 D(x): 0.9821 D(G(z)): 0.0554 / 0.0125 [610/1000][0/4] Loss_D: 0.0633 Loss_G: 4.3795 D(x): 0.9825 D(G(z)): 0.0435 / 0.0186 [611/1000][0/4] Loss_D: 0.0642 Loss_G: 4.3447 D(x): 0.9712 D(G(z)): 0.0334 / 0.0199 [612/1000][0/4] Loss_D: 0.0721 Loss_G: 4.6963 D(x): 0.9866 D(G(z)): 0.0547 / 0.0135 [613/1000][0/4] Loss_D: 0.0574 Loss_G: 4.2039 D(x): 0.9769 D(G(z)): 0.0325 / 0.0237 [614/1000][0/4] Loss_D: 0.0608 Loss_G: 3.7991 D(x): 0.9586 D(G(z)): 0.0180 / 0.0336 [615/1000][0/4] Loss_D: 0.0581 Loss_G: 4.0309 D(x): 0.9722 D(G(z)): 0.0284 / 0.0277 [616/1000][0/4] Loss_D: 0.1187 Loss_G: 5.6928 D(x): 0.9915 D(G(z)): 0.0998 / 0.0052 [617/1000][0/4] Loss_D: 2.3085 Loss_G: 23.4455 D(x): 0.9709 D(G(z)): 0.7974 / 0.0000 [618/1000][0/4] Loss_D: 37.1179 Loss_G: 1.4654 D(x): 0.9441 D(G(z)): 0.8124 / 0.6307 [619/1000][0/4] Loss_D: 6.2669 Loss_G: 1.1541 D(x): 0.0935 D(G(z)): 0.1558 / 0.4369 [620/1000][0/4] Loss_D: 2.4404 Loss_G: 1.8916 D(x): 0.6320 D(G(z)): 0.4702 / 0.3609 [621/1000][0/4] Loss_D: 1.7130 Loss_G: 1.8265 D(x): 0.7064 D(G(z)): 0.6117 / 0.2396 [622/1000][0/4] Loss_D: 1.2610 Loss_G: 0.9213 D(x): 0.5360 D(G(z)): 0.3867 / 0.4342 [623/1000][0/4] Loss_D: 1.4637 Loss_G: 0.6433 D(x): 0.4073 D(G(z)): 0.3362 / 0.5533 [624/1000][0/4] Loss_D: 1.3871 Loss_G: 0.7779 D(x): 0.4475 D(G(z)): 0.3269 / 0.4906 [625/1000][0/4] Loss_D: 1.3843 Loss_G: 0.6040 D(x): 0.4428 D(G(z)): 0.3520 / 0.5751 [626/1000][0/4] Loss_D: 1.4692 Loss_G: 0.9172 D(x): 0.4425 D(G(z)): 0.3889 / 0.4394 [627/1000][0/4] Loss_D: 1.2792 Loss_G: 0.9262 D(x): 0.5150 D(G(z)): 0.3967 / 0.4284 [628/1000][0/4] Loss_D: 1.3468 Loss_G: 1.0053 D(x): 0.3713 D(G(z)): 0.2055 / 0.4086 [629/1000][0/4] Loss_D: 1.4236 Loss_G: 1.3644 D(x): 0.5307 D(G(z)): 0.5023 / 0.2952 [630/1000][0/4] Loss_D: 1.2777 Loss_G: 1.4880 D(x): 0.6566 D(G(z)): 0.5315 / 0.2729 [631/1000][0/4] Loss_D: 1.2844 Loss_G: 1.7368 D(x): 0.6754 D(G(z)): 0.5445 / 0.2199 [632/1000][0/4] Loss_D: 1.7226 Loss_G: 2.0914 D(x): 0.6847 D(G(z)): 0.6782 / 0.1712 [633/1000][0/4] Loss_D: 1.1959 Loss_G: 1.8118 D(x): 0.6621 D(G(z)): 0.4772 / 0.2117 [634/1000][0/4] Loss_D: 1.1115 Loss_G: 1.2959 D(x): 0.5955 D(G(z)): 0.3775 / 0.3160 [635/1000][0/4] Loss_D: 1.5178 Loss_G: 0.5599 D(x): 0.3612 D(G(z)): 0.2642 / 0.6022 [636/1000][0/4] Loss_D: 1.2527 Loss_G: 0.9252 D(x): 0.4828 D(G(z)): 0.3503 / 0.4251 [637/1000][0/4] Loss_D: 1.0298 Loss_G: 1.7641 D(x): 0.6815 D(G(z)): 0.4388 / 0.2161 [638/1000][0/4] Loss_D: 1.2894 Loss_G: 2.2212 D(x): 0.7569 D(G(z)): 0.5758 / 0.1504 [639/1000][0/4] Loss_D: 1.4113 Loss_G: 2.3746 D(x): 0.8050 D(G(z)): 0.6420 / 0.1336 [640/1000][0/4] Loss_D: 1.0734 Loss_G: 1.7679 D(x): 0.6428 D(G(z)): 0.4186 / 0.2136 [641/1000][0/4] Loss_D: 1.2497 Loss_G: 2.5207 D(x): 0.7175 D(G(z)): 0.5402 / 0.1160 [642/1000][0/4] Loss_D: 1.1276 Loss_G: 1.6823 D(x): 0.8318 D(G(z)): 0.5619 / 0.2453 [643/1000][0/4] Loss_D: 1.0055 Loss_G: 1.0810 D(x): 0.5210 D(G(z)): 0.2143 / 0.3807 [644/1000][0/4] Loss_D: 1.3025 Loss_G: 1.0637 D(x): 0.3996 D(G(z)): 0.2081 / 0.3916 [645/1000][0/4] Loss_D: 1.0403 Loss_G: 1.9477 D(x): 0.6147 D(G(z)): 0.3560 / 0.1990 [646/1000][0/4] Loss_D: 1.3240 Loss_G: 3.8886 D(x): 0.8825 D(G(z)): 0.6250 / 0.0441 [647/1000][0/4] Loss_D: 0.9296 Loss_G: 1.8023 D(x): 0.6299 D(G(z)): 0.3108 / 0.2092 [648/1000][0/4] Loss_D: 0.7886 Loss_G: 2.6680 D(x): 0.7303 D(G(z)): 0.3229 / 0.1077 [649/1000][0/4] Loss_D: 1.2958 Loss_G: 5.9975 D(x): 0.9274 D(G(z)): 0.5905 / 0.0101 [650/1000][0/4] Loss_D: 0.7561 Loss_G: 2.4671 D(x): 0.7471 D(G(z)): 0.3063 / 0.1314 [651/1000][0/4] Loss_D: 1.0477 Loss_G: 1.3186 D(x): 0.4976 D(G(z)): 0.1183 / 0.3549 [652/1000][0/4] Loss_D: 0.6241 Loss_G: 3.2562 D(x): 0.7537 D(G(z)): 0.2337 / 0.0824 [653/1000][0/4] Loss_D: 1.3199 Loss_G: 7.9641 D(x): 0.9431 D(G(z)): 0.6190 / 0.0041 [654/1000][0/4] Loss_D: 0.6503 Loss_G: 2.2746 D(x): 0.7603 D(G(z)): 0.2637 / 0.1626 [655/1000][0/4] Loss_D: 0.9441 Loss_G: 1.5296 D(x): 0.5188 D(G(z)): 0.1217 / 0.2930 [656/1000][0/4] Loss_D: 0.5145 Loss_G: 3.6486 D(x): 0.7827 D(G(z)): 0.1920 / 0.0556 [657/1000][0/4] Loss_D: 0.5013 Loss_G: 3.9099 D(x): 0.8503 D(G(z)): 0.2465 / 0.0382 [658/1000][0/4] Loss_D: 0.7015 Loss_G: 7.1483 D(x): 0.9296 D(G(z)): 0.4020 / 0.0033 [659/1000][0/4] Loss_D: 0.7253 Loss_G: 3.2341 D(x): 0.8731 D(G(z)): 0.3227 / 0.1012 [660/1000][0/4] Loss_D: 0.4567 Loss_G: 3.6891 D(x): 0.8447 D(G(z)): 0.1954 / 0.0459 [661/1000][0/4] Loss_D: 0.4594 Loss_G: 3.4353 D(x): 0.7991 D(G(z)): 0.1638 / 0.0603 [662/1000][0/4] Loss_D: 0.3663 Loss_G: 3.4540 D(x): 0.8268 D(G(z)): 0.1281 / 0.0567 [663/1000][0/4] Loss_D: 0.3449 Loss_G: 3.4982 D(x): 0.8253 D(G(z)): 0.1125 / 0.0562 [664/1000][0/4] Loss_D: 0.3440 Loss_G: 4.8794 D(x): 0.8750 D(G(z)): 0.1476 / 0.0185 [665/1000][0/4] Loss_D: 0.3450 Loss_G: 5.3334 D(x): 0.9377 D(G(z)): 0.2134 / 0.0103 [666/1000][0/4] Loss_D: 0.5519 Loss_G: 5.6151 D(x): 0.9683 D(G(z)): 0.3454 / 0.0073 [667/1000][0/4] Loss_D: 0.2347 Loss_G: 3.9420 D(x): 0.8785 D(G(z)): 0.0840 / 0.0386 [668/1000][0/4] Loss_D: 0.2138 Loss_G: 3.1503 D(x): 0.8937 D(G(z)): 0.0879 / 0.0667 [669/1000][0/4] Loss_D: 0.2401 Loss_G: 4.5297 D(x): 0.9457 D(G(z)): 0.1537 / 0.0200 [670/1000][0/4] Loss_D: 0.2115 Loss_G: 3.0892 D(x): 0.8726 D(G(z)): 0.0615 / 0.0713 [671/1000][0/4] Loss_D: 0.1788 Loss_G: 4.2222 D(x): 0.9286 D(G(z)): 0.0918 / 0.0247 [672/1000][0/4] Loss_D: 0.1850 Loss_G: 3.4585 D(x): 0.9111 D(G(z)): 0.0806 / 0.0523 [673/1000][0/4] Loss_D: 0.1790 Loss_G: 3.9151 D(x): 0.9172 D(G(z)): 0.0809 / 0.0359 [674/1000][0/4] Loss_D: 0.1467 Loss_G: 3.5292 D(x): 0.9214 D(G(z)): 0.0588 / 0.0449 [675/1000][0/4] Loss_D: 0.2430 Loss_G: 5.1059 D(x): 0.9666 D(G(z)): 0.1746 / 0.0102 [676/1000][0/4] Loss_D: 0.1539 Loss_G: 3.2296 D(x): 0.9013 D(G(z)): 0.0428 / 0.0665 [677/1000][0/4] Loss_D: 0.1346 Loss_G: 4.1418 D(x): 0.9502 D(G(z)): 0.0758 / 0.0258 [678/1000][0/4] Loss_D: 0.1327 Loss_G: 3.6985 D(x): 0.9362 D(G(z)): 0.0620 / 0.0385 [679/1000][0/4] Loss_D: 0.1421 Loss_G: 3.8422 D(x): 0.9565 D(G(z)): 0.0882 / 0.0334 [680/1000][0/4] Loss_D: 0.1617 Loss_G: 3.1846 D(x): 0.8828 D(G(z)): 0.0310 / 0.0668 [681/1000][0/4] Loss_D: 0.1336 Loss_G: 3.8619 D(x): 0.9616 D(G(z)): 0.0866 / 0.0299 [682/1000][0/4] Loss_D: 0.1158 Loss_G: 4.1430 D(x): 0.9638 D(G(z)): 0.0731 / 0.0238 [683/1000][0/4] Loss_D: 0.1396 Loss_G: 3.6592 D(x): 0.9350 D(G(z)): 0.0673 / 0.0390 [684/1000][0/4] Loss_D: 0.1365 Loss_G: 4.3576 D(x): 0.9622 D(G(z)): 0.0898 / 0.0196 [685/1000][0/4] Loss_D: 0.1332 Loss_G: 3.7607 D(x): 0.9607 D(G(z)): 0.0840 / 0.0373 [686/1000][0/4] Loss_D: 0.1082 Loss_G: 3.5345 D(x): 0.9293 D(G(z)): 0.0324 / 0.0444 [687/1000][0/4] Loss_D: 0.1169 Loss_G: 4.1619 D(x): 0.9638 D(G(z)): 0.0739 / 0.0244 [688/1000][0/4] Loss_D: 0.1349 Loss_G: 3.1253 D(x): 0.9183 D(G(z)): 0.0451 / 0.0662 [689/1000][0/4] Loss_D: 0.1615 Loss_G: 2.5450 D(x): 0.8708 D(G(z)): 0.0197 / 0.1124 [690/1000][0/4] Loss_D: 0.1614 Loss_G: 2.5384 D(x): 0.8890 D(G(z)): 0.0354 / 0.1166 [691/1000][0/4] Loss_D: 1.0159 Loss_G: 1.2229 D(x): 0.4454 D(G(z)): 0.0120 / 0.4248 [692/1000][0/4] Loss_D: 2.2488 Loss_G: 1.2048 D(x): 0.2149 D(G(z)): 0.0578 / 0.4122 [693/1000][0/4] Loss_D: 1.2322 Loss_G: 3.6310 D(x): 0.8279 D(G(z)): 0.5742 / 0.0623 [694/1000][0/4] Loss_D: 1.1132 Loss_G: 4.0353 D(x): 0.9286 D(G(z)): 0.5688 / 0.0328 [695/1000][0/4] Loss_D: 0.6160 Loss_G: 3.1747 D(x): 0.8205 D(G(z)): 0.2761 / 0.0717 [696/1000][0/4] Loss_D: 1.3362 Loss_G: 7.3792 D(x): 0.9855 D(G(z)): 0.6246 / 0.0029 [697/1000][0/4] Loss_D: 0.4412 Loss_G: 3.6820 D(x): 0.7300 D(G(z)): 0.0455 / 0.0528 [698/1000][0/4] Loss_D: 0.4679 Loss_G: 4.4263 D(x): 0.9460 D(G(z)): 0.2805 / 0.0229 [699/1000][0/4] Loss_D: 0.2939 Loss_G: 3.4852 D(x): 0.8683 D(G(z)): 0.1186 / 0.0521 [700/1000][0/4] Loss_D: 0.2252 Loss_G: 3.8260 D(x): 0.8529 D(G(z)): 0.0503 / 0.0421 [701/1000][0/4] Loss_D: 0.1666 Loss_G: 4.1410 D(x): 0.9417 D(G(z)): 0.0943 / 0.0284 [702/1000][0/4] Loss_D: 0.2016 Loss_G: 4.9026 D(x): 0.9654 D(G(z)): 0.1416 / 0.0122 [703/1000][0/4] Loss_D: 0.1507 Loss_G: 3.9423 D(x): 0.9301 D(G(z)): 0.0701 / 0.0328 [704/1000][0/4] Loss_D: 0.2038 Loss_G: 4.7406 D(x): 0.9676 D(G(z)): 0.1454 / 0.0143 [705/1000][0/4] Loss_D: 0.1432 Loss_G: 3.9305 D(x): 0.9330 D(G(z)): 0.0661 / 0.0319 [706/1000][0/4] Loss_D: 0.1538 Loss_G: 3.3908 D(x): 0.8990 D(G(z)): 0.0415 / 0.0539 [707/1000][0/4] Loss_D: 0.1546 Loss_G: 4.1893 D(x): 0.9621 D(G(z)): 0.1046 / 0.0223 [708/1000][0/4] Loss_D: 0.1140 Loss_G: 4.2218 D(x): 0.9617 D(G(z)): 0.0695 / 0.0216 [709/1000][0/4] Loss_D: 0.1339 Loss_G: 3.8694 D(x): 0.9508 D(G(z)): 0.0760 / 0.0308 [710/1000][0/4] Loss_D: 0.1107 Loss_G: 3.8384 D(x): 0.9492 D(G(z)): 0.0545 / 0.0343 [711/1000][0/4] Loss_D: 0.1424 Loss_G: 3.3632 D(x): 0.8997 D(G(z)): 0.0324 / 0.0562 [712/1000][0/4] Loss_D: 0.2375 Loss_G: 5.8140 D(x): 0.9873 D(G(z)): 0.1855 / 0.0048 [713/1000][0/4] Loss_D: 0.1059 Loss_G: 3.9965 D(x): 0.9352 D(G(z)): 0.0353 / 0.0302 [714/1000][0/4] Loss_D: 0.1050 Loss_G: 3.9983 D(x): 0.9654 D(G(z)): 0.0655 / 0.0263 [715/1000][0/4] Loss_D: 0.1001 Loss_G: 3.9771 D(x): 0.9540 D(G(z)): 0.0494 / 0.0284 [716/1000][0/4] Loss_D: 0.1148 Loss_G: 3.3653 D(x): 0.9235 D(G(z)): 0.0327 / 0.0500 [717/1000][0/4] Loss_D: 0.0881 Loss_G: 3.7472 D(x): 0.9548 D(G(z)): 0.0399 / 0.0353 [718/1000][0/4] Loss_D: 0.1145 Loss_G: 4.2234 D(x): 0.9640 D(G(z)): 0.0722 / 0.0223 [719/1000][0/4] Loss_D: 0.0998 Loss_G: 3.9118 D(x): 0.9356 D(G(z)): 0.0313 / 0.0311 [720/1000][0/4] Loss_D: 0.0898 Loss_G: 3.8220 D(x): 0.9525 D(G(z)): 0.0392 / 0.0322 [721/1000][0/4] Loss_D: 0.0961 Loss_G: 4.0393 D(x): 0.9544 D(G(z)): 0.0464 / 0.0275 [722/1000][0/4] Loss_D: 0.1130 Loss_G: 4.1678 D(x): 0.9656 D(G(z)): 0.0721 / 0.0229 [723/1000][0/4] Loss_D: 0.1128 Loss_G: 4.7681 D(x): 0.9770 D(G(z)): 0.0816 / 0.0135 [724/1000][0/4] Loss_D: 0.1097 Loss_G: 3.3411 D(x): 0.9154 D(G(z)): 0.0194 / 0.0524 [725/1000][0/4] Loss_D: 0.1035 Loss_G: 3.9882 D(x): 0.9750 D(G(z)): 0.0725 / 0.0280 [726/1000][0/4] Loss_D: 0.0701 Loss_G: 4.3974 D(x): 0.9730 D(G(z)): 0.0408 / 0.0196 [727/1000][0/4] Loss_D: 0.0753 Loss_G: 3.9309 D(x): 0.9607 D(G(z)): 0.0336 / 0.0295 [728/1000][0/4] Loss_D: 0.0787 Loss_G: 3.8163 D(x): 0.9548 D(G(z)): 0.0309 / 0.0319 [729/1000][0/4] Loss_D: 0.0904 Loss_G: 3.4071 D(x): 0.9379 D(G(z)): 0.0247 / 0.0511 [730/1000][0/4] Loss_D: 0.0864 Loss_G: 3.8260 D(x): 0.9596 D(G(z)): 0.0428 / 0.0319 [731/1000][0/4] Loss_D: 0.0721 Loss_G: 4.1639 D(x): 0.9771 D(G(z)): 0.0468 / 0.0217 [732/1000][0/4] Loss_D: 0.0769 Loss_G: 3.6465 D(x): 0.9578 D(G(z)): 0.0325 / 0.0377 [733/1000][0/4] Loss_D: 0.0766 Loss_G: 3.9777 D(x): 0.9669 D(G(z)): 0.0411 / 0.0273 [734/1000][0/4] Loss_D: 0.0816 Loss_G: 4.4218 D(x): 0.9793 D(G(z)): 0.0573 / 0.0174 [735/1000][0/4] Loss_D: 0.0730 Loss_G: 3.7686 D(x): 0.9485 D(G(z)): 0.0193 / 0.0345 [736/1000][0/4] Loss_D: 0.0720 Loss_G: 3.9850 D(x): 0.9625 D(G(z)): 0.0324 / 0.0271 [737/1000][0/4] Loss_D: 0.0641 Loss_G: 4.2270 D(x): 0.9705 D(G(z)): 0.0331 / 0.0219 [738/1000][0/4] Loss_D: 0.0807 Loss_G: 4.4169 D(x): 0.9809 D(G(z)): 0.0575 / 0.0183 [739/1000][0/4] Loss_D: 0.0747 Loss_G: 4.0417 D(x): 0.9696 D(G(z)): 0.0417 / 0.0256 [740/1000][0/4] Loss_D: 0.0652 Loss_G: 4.1263 D(x): 0.9788 D(G(z)): 0.0419 / 0.0233 [741/1000][0/4] Loss_D: 0.0976 Loss_G: 3.2245 D(x): 0.9243 D(G(z)): 0.0176 / 0.0621 [742/1000][0/4] Loss_D: 0.0836 Loss_G: 4.3690 D(x): 0.9860 D(G(z)): 0.0654 / 0.0180 [743/1000][0/4] Loss_D: 0.0703 Loss_G: 4.3043 D(x): 0.9791 D(G(z)): 0.0464 / 0.0199 [744/1000][0/4] Loss_D: 0.0735 Loss_G: 4.4262 D(x): 0.9782 D(G(z)): 0.0489 / 0.0174 [745/1000][0/4] Loss_D: 0.0557 Loss_G: 4.3838 D(x): 0.9746 D(G(z)): 0.0289 / 0.0191 [746/1000][0/4] Loss_D: 0.0687 Loss_G: 4.4896 D(x): 0.9826 D(G(z)): 0.0486 / 0.0170 [747/1000][0/4] Loss_D: 0.0750 Loss_G: 4.5512 D(x): 0.9863 D(G(z)): 0.0578 / 0.0153 [748/1000][0/4] Loss_D: 0.0556 Loss_G: 4.7950 D(x): 0.9759 D(G(z)): 0.0301 / 0.0128 [749/1000][0/4] Loss_D: 0.0678 Loss_G: 4.2147 D(x): 0.9708 D(G(z)): 0.0366 / 0.0213 [750/1000][0/4] Loss_D: 0.0610 Loss_G: 4.1668 D(x): 0.9727 D(G(z)): 0.0322 / 0.0228 [751/1000][0/4] Loss_D: 0.0811 Loss_G: 3.6760 D(x): 0.9462 D(G(z)): 0.0237 / 0.0385 [752/1000][0/4] Loss_D: 0.0638 Loss_G: 3.6421 D(x): 0.9619 D(G(z)): 0.0242 / 0.0379 [753/1000][0/4] Loss_D: 0.0709 Loss_G: 4.3339 D(x): 0.9829 D(G(z)): 0.0511 / 0.0190 [754/1000][0/4] Loss_D: 0.0913 Loss_G: 5.0556 D(x): 0.9856 D(G(z)): 0.0714 / 0.0095 [755/1000][0/4] Loss_D: 0.0522 Loss_G: 4.5490 D(x): 0.9738 D(G(z)): 0.0249 / 0.0162 [756/1000][0/4] Loss_D: 0.0544 Loss_G: 4.5119 D(x): 0.9838 D(G(z)): 0.0364 / 0.0167 [757/1000][0/4] Loss_D: 0.0558 Loss_G: 4.2025 D(x): 0.9694 D(G(z)): 0.0237 / 0.0230 [758/1000][0/4] Loss_D: 0.0603 Loss_G: 4.3444 D(x): 0.9779 D(G(z)): 0.0364 / 0.0211 [759/1000][0/4] Loss_D: 0.0499 Loss_G: 4.2830 D(x): 0.9741 D(G(z)): 0.0229 / 0.0207 [760/1000][0/4] Loss_D: 0.0498 Loss_G: 4.2661 D(x): 0.9726 D(G(z)): 0.0214 / 0.0207 [761/1000][0/4] Loss_D: 0.0593 Loss_G: 4.6487 D(x): 0.9813 D(G(z)): 0.0389 / 0.0134 [762/1000][0/4] Loss_D: 0.0610 Loss_G: 4.0943 D(x): 0.9594 D(G(z)): 0.0185 / 0.0293 [763/1000][0/4] Loss_D: 0.0502 Loss_G: 4.2524 D(x): 0.9688 D(G(z)): 0.0179 / 0.0224 [764/1000][0/4] Loss_D: 0.0493 Loss_G: 4.4362 D(x): 0.9788 D(G(z)): 0.0270 / 0.0188 [765/1000][0/4] Loss_D: 0.0700 Loss_G: 5.0589 D(x): 0.9883 D(G(z)): 0.0551 / 0.0099 [766/1000][0/4] Loss_D: 0.0756 Loss_G: 3.3722 D(x): 0.9440 D(G(z)): 0.0171 / 0.0544 [767/1000][0/4] Loss_D: 0.0530 Loss_G: 4.3691 D(x): 0.9699 D(G(z)): 0.0217 / 0.0193 [768/1000][0/4] Loss_D: 0.0561 Loss_G: 4.5080 D(x): 0.9851 D(G(z)): 0.0392 / 0.0165 [769/1000][0/4] Loss_D: 0.0460 Loss_G: 4.5107 D(x): 0.9779 D(G(z)): 0.0229 / 0.0166 [770/1000][0/4] Loss_D: 0.0559 Loss_G: 4.4255 D(x): 0.9793 D(G(z)): 0.0337 / 0.0178 [771/1000][0/4] Loss_D: 0.0706 Loss_G: 4.9906 D(x): 0.9869 D(G(z)): 0.0542 / 0.0103 [772/1000][0/4] Loss_D: 0.0480 Loss_G: 4.4295 D(x): 0.9841 D(G(z)): 0.0310 / 0.0172 [773/1000][0/4] Loss_D: 0.0457 Loss_G: 4.7928 D(x): 0.9847 D(G(z)): 0.0290 / 0.0134 [774/1000][0/4] Loss_D: 0.0874 Loss_G: 2.6893 D(x): 0.9278 D(G(z)): 0.0108 / 0.1143 [775/1000][0/4] Loss_D: 0.9293 Loss_G: 1.0513 D(x): 0.5031 D(G(z)): 0.0019 / 0.4578 [776/1000][0/4] Loss_D: 1.3885 Loss_G: 1.2579 D(x): 0.4921 D(G(z)): 0.2989 / 0.3603 [777/1000][0/4] Loss_D: 0.9769 Loss_G: 1.3482 D(x): 0.5954 D(G(z)): 0.2770 / 0.3145 [778/1000][0/4] Loss_D: 0.8003 Loss_G: 1.5649 D(x): 0.5700 D(G(z)): 0.1081 / 0.2746 [779/1000][0/4] Loss_D: 0.4614 Loss_G: 3.8251 D(x): 0.8155 D(G(z)): 0.1748 / 0.0552 [780/1000][0/4] Loss_D: 0.2575 Loss_G: 4.7008 D(x): 0.9423 D(G(z)): 0.1610 / 0.0213 [781/1000][0/4] Loss_D: 0.1489 Loss_G: 3.4668 D(x): 0.9211 D(G(z)): 0.0567 / 0.0528 [782/1000][0/4] Loss_D: 0.3246 Loss_G: 3.7302 D(x): 0.7666 D(G(z)): 0.0124 / 0.0578 [783/1000][0/4] Loss_D: 0.1289 Loss_G: 3.8999 D(x): 0.9315 D(G(z)): 0.0493 / 0.0337 [784/1000][0/4] Loss_D: 0.1152 Loss_G: 4.1635 D(x): 0.9547 D(G(z)): 0.0631 / 0.0239 [785/1000][0/4] Loss_D: 0.1276 Loss_G: 4.8253 D(x): 0.9793 D(G(z)): 0.0944 / 0.0139 [786/1000][0/4] Loss_D: 0.1245 Loss_G: 5.0965 D(x): 0.9865 D(G(z)): 0.0991 / 0.0101 [787/1000][0/4] Loss_D: 0.0878 Loss_G: 4.5994 D(x): 0.9804 D(G(z)): 0.0634 / 0.0155 [788/1000][0/4] Loss_D: 0.0671 Loss_G: 4.5216 D(x): 0.9814 D(G(z)): 0.0462 / 0.0178 [789/1000][0/4] Loss_D: 0.0789 Loss_G: 4.4006 D(x): 0.9670 D(G(z)): 0.0424 / 0.0193 [790/1000][0/4] Loss_D: 0.0745 Loss_G: 4.1806 D(x): 0.9601 D(G(z)): 0.0318 / 0.0239 [791/1000][0/4] Loss_D: 0.0783 Loss_G: 4.2938 D(x): 0.9691 D(G(z)): 0.0443 / 0.0214 [792/1000][0/4] Loss_D: 0.0750 Loss_G: 4.2158 D(x): 0.9702 D(G(z)): 0.0424 / 0.0229 [793/1000][0/4] Loss_D: 0.0798 Loss_G: 4.5048 D(x): 0.9848 D(G(z)): 0.0607 / 0.0163 [794/1000][0/4] Loss_D: 0.0630 Loss_G: 4.3989 D(x): 0.9768 D(G(z)): 0.0380 / 0.0189 [795/1000][0/4] Loss_D: 0.0593 Loss_G: 4.3091 D(x): 0.9706 D(G(z)): 0.0285 / 0.0200 [796/1000][0/4] Loss_D: 0.0659 Loss_G: 4.1016 D(x): 0.9711 D(G(z)): 0.0350 / 0.0260 [797/1000][0/4] Loss_D: 0.0671 Loss_G: 4.0662 D(x): 0.9621 D(G(z)): 0.0272 / 0.0262 [798/1000][0/4] Loss_D: 0.0583 Loss_G: 4.2677 D(x): 0.9691 D(G(z)): 0.0260 / 0.0218 [799/1000][0/4] Loss_D: 0.0606 Loss_G: 4.1798 D(x): 0.9703 D(G(z)): 0.0292 / 0.0236 [800/1000][0/4] Loss_D: 0.0972 Loss_G: 5.0678 D(x): 0.9916 D(G(z)): 0.0818 / 0.0095 [801/1000][0/4] Loss_D: 0.0687 Loss_G: 4.8033 D(x): 0.9820 D(G(z)): 0.0475 / 0.0132 [802/1000][0/4] Loss_D: 0.0621 Loss_G: 4.4066 D(x): 0.9745 D(G(z)): 0.0346 / 0.0197 [803/1000][0/4] Loss_D: 0.0558 Loss_G: 4.5317 D(x): 0.9840 D(G(z)): 0.0381 / 0.0162 [804/1000][0/4] Loss_D: 0.0500 Loss_G: 4.2387 D(x): 0.9701 D(G(z)): 0.0191 / 0.0205 [805/1000][0/4] Loss_D: 0.0535 Loss_G: 4.5803 D(x): 0.9778 D(G(z)): 0.0300 / 0.0153 [806/1000][0/4] Loss_D: 0.0497 Loss_G: 4.6803 D(x): 0.9833 D(G(z)): 0.0318 / 0.0143 [807/1000][0/4] Loss_D: 0.0548 Loss_G: 4.2809 D(x): 0.9741 D(G(z)): 0.0275 / 0.0212 [808/1000][0/4] Loss_D: 0.0527 Loss_G: 4.3027 D(x): 0.9742 D(G(z)): 0.0256 / 0.0216 [809/1000][0/4] Loss_D: 0.0478 Loss_G: 4.3000 D(x): 0.9761 D(G(z)): 0.0229 / 0.0207 [810/1000][0/4] Loss_D: 0.0439 Loss_G: 4.4365 D(x): 0.9757 D(G(z)): 0.0188 / 0.0174 [811/1000][0/4] Loss_D: 0.0541 Loss_G: 4.4822 D(x): 0.9831 D(G(z)): 0.0356 / 0.0173 [812/1000][0/4] Loss_D: 0.1029 Loss_G: 5.4040 D(x): 0.9935 D(G(z)): 0.0883 / 0.0063 [813/1000][0/4] Loss_D: 0.0392 Loss_G: 4.7583 D(x): 0.9768 D(G(z)): 0.0154 / 0.0138 [814/1000][0/4] Loss_D: 0.0460 Loss_G: 4.5821 D(x): 0.9685 D(G(z)): 0.0133 / 0.0158 [815/1000][0/4] Loss_D: 0.0477 Loss_G: 4.3547 D(x): 0.9715 D(G(z)): 0.0183 / 0.0196 [816/1000][0/4] Loss_D: 0.0471 Loss_G: 4.2439 D(x): 0.9763 D(G(z)): 0.0225 / 0.0220 [817/1000][0/4] Loss_D: 0.1107 Loss_G: 5.6263 D(x): 0.9919 D(G(z)): 0.0938 / 0.0053 [818/1000][0/4] Loss_D: 0.0580 Loss_G: 4.2446 D(x): 0.9573 D(G(z)): 0.0136 / 0.0235 [819/1000][0/4] Loss_D: 0.0403 Loss_G: 4.5389 D(x): 0.9744 D(G(z)): 0.0139 / 0.0172 [820/1000][0/4] Loss_D: 0.0411 Loss_G: 4.5274 D(x): 0.9851 D(G(z)): 0.0253 / 0.0162 [821/1000][0/4] Loss_D: 0.0547 Loss_G: 4.8726 D(x): 0.9898 D(G(z)): 0.0424 / 0.0115 [822/1000][0/4] Loss_D: 0.0476 Loss_G: 4.6245 D(x): 0.9827 D(G(z)): 0.0291 / 0.0152 [823/1000][0/4] Loss_D: 0.0338 Loss_G: 4.8997 D(x): 0.9848 D(G(z)): 0.0180 / 0.0113 [824/1000][0/4] Loss_D: 0.0356 Loss_G: 4.6485 D(x): 0.9789 D(G(z)): 0.0141 / 0.0138 [825/1000][0/4] Loss_D: 0.0447 Loss_G: 4.2133 D(x): 0.9720 D(G(z)): 0.0159 / 0.0225 [826/1000][0/4] Loss_D: 0.0473 Loss_G: 4.5480 D(x): 0.9841 D(G(z)): 0.0301 / 0.0161 [827/1000][0/4] Loss_D: 0.0429 Loss_G: 4.7629 D(x): 0.9836 D(G(z)): 0.0255 / 0.0130 [828/1000][0/4] Loss_D: 0.0432 Loss_G: 4.5876 D(x): 0.9862 D(G(z)): 0.0284 / 0.0152 [829/1000][0/4] Loss_D: 0.0469 Loss_G: 4.8200 D(x): 0.9904 D(G(z)): 0.0358 / 0.0128 [830/1000][0/4] Loss_D: 0.0394 Loss_G: 4.8608 D(x): 0.9814 D(G(z)): 0.0202 / 0.0129 [831/1000][0/4] Loss_D: 0.0373 Loss_G: 4.6837 D(x): 0.9838 D(G(z)): 0.0204 / 0.0140 [832/1000][0/4] Loss_D: 0.0419 Loss_G: 4.4136 D(x): 0.9704 D(G(z)): 0.0116 / 0.0181 [833/1000][0/4] Loss_D: 0.0587 Loss_G: 5.1547 D(x): 0.9906 D(G(z)): 0.0467 / 0.0089 [834/1000][0/4] Loss_D: 0.0372 Loss_G: 4.8799 D(x): 0.9847 D(G(z)): 0.0212 / 0.0129 [835/1000][0/4] Loss_D: 0.0384 Loss_G: 4.6227 D(x): 0.9834 D(G(z)): 0.0211 / 0.0149 [836/1000][0/4] Loss_D: 0.0374 Loss_G: 4.6908 D(x): 0.9855 D(G(z)): 0.0222 / 0.0139 [837/1000][0/4] Loss_D: 0.0428 Loss_G: 5.2581 D(x): 0.9919 D(G(z)): 0.0332 / 0.0082 [838/1000][0/4] Loss_D: 0.3954 Loss_G: 11.3641 D(x): 0.9980 D(G(z)): 0.2924 / 0.0000 [839/1000][0/4] Loss_D: 6.3494 Loss_G: 0.0053 D(x): 0.0669 D(G(z)): 0.0513 / 0.9953 [840/1000][0/4] Loss_D: 1.4546 Loss_G: 5.3974 D(x): 0.8054 D(G(z)): 0.5703 / 0.0290 [841/1000][0/4] Loss_D: 1.0937 Loss_G: 2.6828 D(x): 0.8918 D(G(z)): 0.5185 / 0.1285 [842/1000][0/4] Loss_D: 1.0345 Loss_G: 2.1524 D(x): 0.5675 D(G(z)): 0.2602 / 0.2152 [843/1000][0/4] Loss_D: 0.6684 Loss_G: 2.9441 D(x): 0.6237 D(G(z)): 0.0758 / 0.1269 [844/1000][0/4] Loss_D: 0.8563 Loss_G: 2.1314 D(x): 0.5359 D(G(z)): 0.0356 / 0.2115 [845/1000][0/4] Loss_D: 0.3939 Loss_G: 4.4163 D(x): 0.8434 D(G(z)): 0.1496 / 0.0299 [846/1000][0/4] Loss_D: 0.2981 Loss_G: 4.7430 D(x): 0.9255 D(G(z)): 0.1725 / 0.0161 [847/1000][0/4] Loss_D: 0.4694 Loss_G: 7.4604 D(x): 0.9676 D(G(z)): 0.3009 / 0.0017 [848/1000][0/4] Loss_D: 0.1504 Loss_G: 3.7930 D(x): 0.9122 D(G(z)): 0.0442 / 0.0443 [849/1000][0/4] Loss_D: 0.1783 Loss_G: 4.4757 D(x): 0.9365 D(G(z)): 0.0963 / 0.0226 [850/1000][0/4] Loss_D: 0.1656 Loss_G: 4.4024 D(x): 0.9298 D(G(z)): 0.0810 / 0.0218 [851/1000][0/4] Loss_D: 0.1606 Loss_G: 4.1269 D(x): 0.9133 D(G(z)): 0.0606 / 0.0300 [852/1000][0/4] Loss_D: 0.1407 Loss_G: 5.1010 D(x): 0.9598 D(G(z)): 0.0885 / 0.0108 [853/1000][0/4] Loss_D: 0.1540 Loss_G: 4.9648 D(x): 0.9678 D(G(z)): 0.1076 / 0.0112 [854/1000][0/4] Loss_D: 0.1100 Loss_G: 4.2714 D(x): 0.9371 D(G(z)): 0.0401 / 0.0229 [855/1000][0/4] Loss_D: 0.1299 Loss_G: 4.6812 D(x): 0.9702 D(G(z)): 0.0887 / 0.0149 [856/1000][0/4] Loss_D: 0.1044 Loss_G: 4.2499 D(x): 0.9412 D(G(z)): 0.0406 / 0.0254 [857/1000][0/4] Loss_D: 0.1163 Loss_G: 4.4680 D(x): 0.9689 D(G(z)): 0.0776 / 0.0171 [858/1000][0/4] Loss_D: 0.1035 Loss_G: 3.9644 D(x): 0.9376 D(G(z)): 0.0360 / 0.0306 [859/1000][0/4] Loss_D: 0.1016 Loss_G: 4.2392 D(x): 0.9723 D(G(z)): 0.0681 / 0.0234 [860/1000][0/4] Loss_D: 0.0881 Loss_G: 4.2019 D(x): 0.9632 D(G(z)): 0.0478 / 0.0234 [861/1000][0/4] Loss_D: 0.0787 Loss_G: 4.1246 D(x): 0.9550 D(G(z)): 0.0312 / 0.0250 [862/1000][0/4] Loss_D: 0.0888 Loss_G: 4.2187 D(x): 0.9549 D(G(z)): 0.0402 / 0.0233 [863/1000][0/4] Loss_D: 0.0873 Loss_G: 4.8563 D(x): 0.9836 D(G(z)): 0.0662 / 0.0114 [864/1000][0/4] Loss_D: 0.0862 Loss_G: 4.1231 D(x): 0.9414 D(G(z)): 0.0244 / 0.0279 [865/1000][0/4] Loss_D: 0.1523 Loss_G: 5.6772 D(x): 0.9902 D(G(z)): 0.1253 / 0.0054 [866/1000][0/4] Loss_D: 0.0706 Loss_G: 4.4308 D(x): 0.9617 D(G(z)): 0.0298 / 0.0186 [867/1000][0/4] Loss_D: 0.0676 Loss_G: 4.6021 D(x): 0.9757 D(G(z)): 0.0409 / 0.0160 [868/1000][0/4] Loss_D: 0.0592 Loss_G: 4.3789 D(x): 0.9673 D(G(z)): 0.0250 / 0.0196 [869/1000][0/4] Loss_D: 0.0669 Loss_G: 4.2306 D(x): 0.9767 D(G(z)): 0.0413 / 0.0207 [870/1000][0/4] Loss_D: 0.0674 Loss_G: 4.5789 D(x): 0.9637 D(G(z)): 0.0287 / 0.0168 [871/1000][0/4] Loss_D: 0.0667 Loss_G: 4.2384 D(x): 0.9636 D(G(z)): 0.0283 / 0.0220 [872/1000][0/4] Loss_D: 0.0671 Loss_G: 4.0869 D(x): 0.9688 D(G(z)): 0.0341 / 0.0242 [873/1000][0/4] Loss_D: 0.1010 Loss_G: 5.2675 D(x): 0.9888 D(G(z)): 0.0827 / 0.0080 [874/1000][0/4] Loss_D: 0.0499 Loss_G: 4.5680 D(x): 0.9761 D(G(z)): 0.0250 / 0.0161 [875/1000][0/4] Loss_D: 0.0643 Loss_G: 4.4266 D(x): 0.9636 D(G(z)): 0.0260 / 0.0219 [876/1000][0/4] Loss_D: 0.0580 Loss_G: 3.9261 D(x): 0.9613 D(G(z)): 0.0179 / 0.0304 [877/1000][0/4] Loss_D: 0.0724 Loss_G: 3.9908 D(x): 0.9687 D(G(z)): 0.0388 / 0.0292 [878/1000][0/4] Loss_D: 0.0665 Loss_G: 4.4184 D(x): 0.9856 D(G(z)): 0.0493 / 0.0175 [879/1000][0/4] Loss_D: 0.0602 Loss_G: 4.4024 D(x): 0.9825 D(G(z)): 0.0406 / 0.0183 [880/1000][0/4] Loss_D: 0.0544 Loss_G: 4.7884 D(x): 0.9807 D(G(z)): 0.0336 / 0.0131 [881/1000][0/4] Loss_D: 0.0591 Loss_G: 4.5284 D(x): 0.9781 D(G(z)): 0.0353 / 0.0170 [882/1000][0/4] Loss_D: 0.0516 Loss_G: 4.6518 D(x): 0.9791 D(G(z)): 0.0293 / 0.0140 [883/1000][0/4] Loss_D: 0.0513 Loss_G: 4.6471 D(x): 0.9839 D(G(z)): 0.0338 / 0.0148 [884/1000][0/4] Loss_D: 0.0481 Loss_G: 4.3163 D(x): 0.9763 D(G(z)): 0.0235 / 0.0201 [885/1000][0/4] Loss_D: 0.0495 Loss_G: 4.2471 D(x): 0.9702 D(G(z)): 0.0187 / 0.0213 [886/1000][0/4] Loss_D: 0.0629 Loss_G: 3.9180 D(x): 0.9637 D(G(z)): 0.0249 / 0.0324 [887/1000][0/4] Loss_D: 0.0483 Loss_G: 4.3620 D(x): 0.9831 D(G(z)): 0.0303 / 0.0188 [888/1000][0/4] Loss_D: 0.0569 Loss_G: 4.2618 D(x): 0.9761 D(G(z)): 0.0313 / 0.0214 [889/1000][0/4] Loss_D: 0.0580 Loss_G: 4.8957 D(x): 0.9916 D(G(z)): 0.0472 / 0.0115 [890/1000][0/4] Loss_D: 0.0497 Loss_G: 5.0136 D(x): 0.9884 D(G(z)): 0.0368 / 0.0100 [891/1000][0/4] Loss_D: 0.0475 Loss_G: 4.5843 D(x): 0.9799 D(G(z)): 0.0262 / 0.0160 [892/1000][0/4] Loss_D: 0.0481 Loss_G: 4.5147 D(x): 0.9757 D(G(z)): 0.0228 / 0.0167 [893/1000][0/4] Loss_D: 0.0491 Loss_G: 4.6701 D(x): 0.9843 D(G(z)): 0.0322 / 0.0146 [894/1000][0/4] Loss_D: 0.0400 Loss_G: 4.6071 D(x): 0.9820 D(G(z)): 0.0212 / 0.0150 [895/1000][0/4] Loss_D: 0.0415 Loss_G: 4.4928 D(x): 0.9729 D(G(z)): 0.0137 / 0.0186 [896/1000][0/4] Loss_D: 0.0420 Loss_G: 4.5318 D(x): 0.9858 D(G(z)): 0.0269 / 0.0160 [897/1000][0/4] Loss_D: 0.0395 Loss_G: 4.5907 D(x): 0.9773 D(G(z)): 0.0163 / 0.0153 [898/1000][0/4] Loss_D: 0.0460 Loss_G: 4.5725 D(x): 0.9885 D(G(z)): 0.0332 / 0.0163 [899/1000][0/4] Loss_D: 0.0428 Loss_G: 4.6568 D(x): 0.9832 D(G(z)): 0.0250 / 0.0150 [900/1000][0/4] Loss_D: 0.0428 Loss_G: 4.7111 D(x): 0.9828 D(G(z)): 0.0247 / 0.0138 [901/1000][0/4] Loss_D: 0.0448 Loss_G: 4.3746 D(x): 0.9733 D(G(z)): 0.0171 / 0.0204 [902/1000][0/4] Loss_D: 0.0430 Loss_G: 4.5524 D(x): 0.9800 D(G(z)): 0.0218 / 0.0169 [903/1000][0/4] Loss_D: 0.0333 Loss_G: 4.5556 D(x): 0.9818 D(G(z)): 0.0146 / 0.0156 [904/1000][0/4] Loss_D: 0.0639 Loss_G: 3.5000 D(x): 0.9468 D(G(z)): 0.0087 / 0.0496 [905/1000][0/4] Loss_D: 0.0422 Loss_G: 4.7212 D(x): 0.9836 D(G(z)): 0.0249 / 0.0137 [906/1000][0/4] Loss_D: 0.0371 Loss_G: 4.7173 D(x): 0.9846 D(G(z)): 0.0210 / 0.0143 [907/1000][0/4] Loss_D: 0.0387 Loss_G: 4.9486 D(x): 0.9860 D(G(z)): 0.0238 / 0.0115 [908/1000][0/4] Loss_D: 0.0383 Loss_G: 4.3886 D(x): 0.9763 D(G(z)): 0.0134 / 0.0198 [909/1000][0/4] Loss_D: 0.0687 Loss_G: 5.3442 D(x): 0.9939 D(G(z)): 0.0590 / 0.0071 [910/1000][0/4] Loss_D: 0.0416 Loss_G: 4.6560 D(x): 0.9684 D(G(z)): 0.0092 / 0.0148 [911/1000][0/4] Loss_D: 0.0367 Loss_G: 4.7823 D(x): 0.9871 D(G(z)): 0.0230 / 0.0136 [912/1000][0/4] Loss_D: 0.0321 Loss_G: 4.6873 D(x): 0.9842 D(G(z)): 0.0159 / 0.0141 [913/1000][0/4] Loss_D: 0.0411 Loss_G: 4.4374 D(x): 0.9739 D(G(z)): 0.0142 / 0.0205 [914/1000][0/4] Loss_D: 0.0378 Loss_G: 4.3979 D(x): 0.9815 D(G(z)): 0.0187 / 0.0186 [915/1000][0/4] Loss_D: 0.0342 Loss_G: 4.6519 D(x): 0.9854 D(G(z)): 0.0191 / 0.0143 [916/1000][0/4] Loss_D: 0.0547 Loss_G: 3.7199 D(x): 0.9603 D(G(z)): 0.0130 / 0.0396 [917/1000][0/4] Loss_D: 0.0351 Loss_G: 4.5978 D(x): 0.9837 D(G(z)): 0.0183 / 0.0153 [918/1000][0/4] Loss_D: 0.0326 Loss_G: 4.6999 D(x): 0.9818 D(G(z)): 0.0137 / 0.0151 [919/1000][0/4] Loss_D: 0.0452 Loss_G: 4.2423 D(x): 0.9669 D(G(z)): 0.0108 / 0.0248 [920/1000][0/4] Loss_D: 0.0384 Loss_G: 4.8234 D(x): 0.9900 D(G(z)): 0.0271 / 0.0128 [921/1000][0/4] Loss_D: 0.0343 Loss_G: 4.7377 D(x): 0.9818 D(G(z)): 0.0156 / 0.0144 [922/1000][0/4] Loss_D: 0.0433 Loss_G: 5.1786 D(x): 0.9908 D(G(z)): 0.0328 / 0.0093 [923/1000][0/4] Loss_D: 0.0335 Loss_G: 4.7966 D(x): 0.9820 D(G(z)): 0.0150 / 0.0128 [924/1000][0/4] Loss_D: 0.0444 Loss_G: 5.0181 D(x): 0.9902 D(G(z)): 0.0333 / 0.0099 [925/1000][0/4] Loss_D: 0.0425 Loss_G: 4.1970 D(x): 0.9661 D(G(z)): 0.0078 / 0.0238 [926/1000][0/4] Loss_D: 0.0334 Loss_G: 4.5347 D(x): 0.9805 D(G(z)): 0.0135 / 0.0176 [927/1000][0/4] Loss_D: 0.0322 Loss_G: 4.6405 D(x): 0.9844 D(G(z)): 0.0162 / 0.0150 [928/1000][0/4] Loss_D: 0.0370 Loss_G: 4.4046 D(x): 0.9729 D(G(z)): 0.0094 / 0.0204 [929/1000][0/4] Loss_D: 0.0321 Loss_G: 4.6786 D(x): 0.9829 D(G(z)): 0.0146 / 0.0157 [930/1000][0/4] Loss_D: 0.0478 Loss_G: 3.6697 D(x): 0.9610 D(G(z)): 0.0078 / 0.0424 [931/1000][0/4] Loss_D: 0.0378 Loss_G: 5.0245 D(x): 0.9868 D(G(z)): 0.0238 / 0.0102 [932/1000][0/4] Loss_D: 0.0331 Loss_G: 5.0608 D(x): 0.9918 D(G(z)): 0.0242 / 0.0096 [933/1000][0/4] Loss_D: 0.0354 Loss_G: 5.1311 D(x): 0.9885 D(G(z)): 0.0232 / 0.0092 [934/1000][0/4] Loss_D: 0.0344 Loss_G: 4.4802 D(x): 0.9789 D(G(z)): 0.0128 / 0.0185 [935/1000][0/4] Loss_D: 0.0282 Loss_G: 4.7491 D(x): 0.9881 D(G(z)): 0.0160 / 0.0131 [936/1000][0/4] Loss_D: 0.0432 Loss_G: 5.0048 D(x): 0.9918 D(G(z)): 0.0339 / 0.0098 [937/1000][0/4] Loss_D: 0.0290 Loss_G: 5.1220 D(x): 0.9877 D(G(z)): 0.0160 / 0.0092 [938/1000][0/4] Loss_D: 0.0287 Loss_G: 5.0568 D(x): 0.9901 D(G(z)): 0.0183 / 0.0108 [939/1000][0/4] Loss_D: 0.0343 Loss_G: 5.0628 D(x): 0.9912 D(G(z)): 0.0245 / 0.0100 [940/1000][0/4] Loss_D: 0.0323 Loss_G: 4.7683 D(x): 0.9798 D(G(z)): 0.0117 / 0.0136 [941/1000][0/4] Loss_D: 0.0355 Loss_G: 4.6448 D(x): 0.9866 D(G(z)): 0.0215 / 0.0149 [942/1000][0/4] Loss_D: 0.0371 Loss_G: 4.7369 D(x): 0.9775 D(G(z)): 0.0139 / 0.0144 [943/1000][0/4] Loss_D: 0.0309 Loss_G: 4.4498 D(x): 0.9810 D(G(z)): 0.0115 / 0.0186 [944/1000][0/4] Loss_D: 0.0318 Loss_G: 4.6130 D(x): 0.9846 D(G(z)): 0.0159 / 0.0157 [945/1000][0/4] Loss_D: 0.1155 Loss_G: 7.7043 D(x): 0.9972 D(G(z)): 0.1014 / 0.0008 [946/1000][0/4] Loss_D: 2.1957 Loss_G: 5.1388 D(x): 0.7630 D(G(z)): 0.6383 / 0.0249 [947/1000][0/4] Loss_D: 1.4530 Loss_G: 1.5914 D(x): 0.6427 D(G(z)): 0.4745 / 0.2736 [948/1000][0/4] Loss_D: 1.2311 Loss_G: 1.5239 D(x): 0.6219 D(G(z)): 0.4624 / 0.2621 [949/1000][0/4] Loss_D: 1.6407 Loss_G: 2.5813 D(x): 0.8751 D(G(z)): 0.7153 / 0.1161 [950/1000][0/4] Loss_D: 1.0477 Loss_G: 1.1749 D(x): 0.5793 D(G(z)): 0.3330 / 0.3412 [951/1000][0/4] Loss_D: 1.1078 Loss_G: 0.8461 D(x): 0.5098 D(G(z)): 0.2286 / 0.4726 [952/1000][0/4] Loss_D: 1.1378 Loss_G: 1.5185 D(x): 0.4794 D(G(z)): 0.1723 / 0.2804 [953/1000][0/4] Loss_D: 1.0230 Loss_G: 3.0362 D(x): 0.7688 D(G(z)): 0.4243 / 0.1064 [954/1000][0/4] Loss_D: 1.1897 Loss_G: 3.9047 D(x): 0.8456 D(G(z)): 0.5448 / 0.0468 [955/1000][0/4] Loss_D: 0.7074 Loss_G: 2.9901 D(x): 0.8173 D(G(z)): 0.3363 / 0.0902 [956/1000][0/4] Loss_D: 0.8158 Loss_G: 3.0035 D(x): 0.7026 D(G(z)): 0.2848 / 0.1059 [957/1000][0/4] Loss_D: 1.8746 Loss_G: 1.8732 D(x): 0.2787 D(G(z)): 0.0202 / 0.3085 [958/1000][0/4] Loss_D: 0.6756 Loss_G: 3.7724 D(x): 0.8819 D(G(z)): 0.3661 / 0.0549 [959/1000][0/4] Loss_D: 0.7537 Loss_G: 5.1062 D(x): 0.8797 D(G(z)): 0.3648 / 0.0225 [960/1000][0/4] Loss_D: 0.8536 Loss_G: 5.5302 D(x): 0.9499 D(G(z)): 0.4480 / 0.0155 [961/1000][0/4] Loss_D: 0.6601 Loss_G: 2.4023 D(x): 0.6367 D(G(z)): 0.0502 / 0.1800 [962/1000][0/4] Loss_D: 0.3687 Loss_G: 8.4081 D(x): 0.9554 D(G(z)): 0.2295 / 0.0031 [963/1000][0/4] Loss_D: 1.6035 Loss_G: 7.4175 D(x): 0.9884 D(G(z)): 0.6581 / 0.0033 [964/1000][0/4] Loss_D: 0.4976 Loss_G: 4.0817 D(x): 0.7143 D(G(z)): 0.0473 / 0.0426 [965/1000][0/4] Loss_D: 0.2407 Loss_G: 5.0553 D(x): 0.9239 D(G(z)): 0.1238 / 0.0158 [966/1000][0/4] Loss_D: 0.2482 Loss_G: 5.1884 D(x): 0.9256 D(G(z)): 0.1364 / 0.0123 [967/1000][0/4] Loss_D: 0.4676 Loss_G: 9.3768 D(x): 0.9795 D(G(z)): 0.3031 / 0.0008 [968/1000][0/4] Loss_D: 0.1961 Loss_G: 4.3664 D(x): 0.8853 D(G(z)): 0.0574 / 0.0301 [969/1000][0/4] Loss_D: 0.1671 Loss_G: 4.5768 D(x): 0.9161 D(G(z)): 0.0648 / 0.0235 [970/1000][0/4] Loss_D: 0.1615 Loss_G: 5.8823 D(x): 0.9658 D(G(z)): 0.1087 / 0.0072 [971/1000][0/4] Loss_D: 0.2031 Loss_G: 3.9065 D(x): 0.8515 D(G(z)): 0.0212 / 0.0379 [972/1000][0/4] Loss_D: 0.0942 Loss_G: 4.5115 D(x): 0.9555 D(G(z)): 0.0444 / 0.0213 [973/1000][0/4] Loss_D: 0.0955 Loss_G: 4.9339 D(x): 0.9679 D(G(z)): 0.0571 / 0.0138 [974/1000][0/4] Loss_D: 0.1080 Loss_G: 4.7905 D(x): 0.9625 D(G(z)): 0.0634 / 0.0147 [975/1000][0/4] Loss_D: 0.0897 Loss_G: 4.1751 D(x): 0.9437 D(G(z)): 0.0293 / 0.0251 [976/1000][0/4] Loss_D: 0.1083 Loss_G: 4.5732 D(x): 0.9614 D(G(z)): 0.0621 / 0.0184 [977/1000][0/4] Loss_D: 0.0938 Loss_G: 4.9430 D(x): 0.9813 D(G(z)): 0.0691 / 0.0120 [978/1000][0/4] Loss_D: 0.0807 Loss_G: 4.6343 D(x): 0.9640 D(G(z)): 0.0397 / 0.0161 [979/1000][0/4] Loss_D: 0.0656 Loss_G: 4.7958 D(x): 0.9694 D(G(z)): 0.0327 / 0.0139 [980/1000][0/4] Loss_D: 0.0685 Loss_G: 4.7601 D(x): 0.9742 D(G(z)): 0.0401 / 0.0133 [981/1000][0/4] Loss_D: 0.0827 Loss_G: 4.1900 D(x): 0.9521 D(G(z)): 0.0312 / 0.0240 [982/1000][0/4] Loss_D: 0.0726 Loss_G: 4.2447 D(x): 0.9567 D(G(z)): 0.0269 / 0.0230 [983/1000][0/4] Loss_D: 0.0669 Loss_G: 4.1617 D(x): 0.9629 D(G(z)): 0.0275 / 0.0237 [984/1000][0/4] Loss_D: 0.0714 Loss_G: 4.7856 D(x): 0.9711 D(G(z)): 0.0397 / 0.0134 [985/1000][0/4] Loss_D: 0.0494 Loss_G: 4.3924 D(x): 0.9688 D(G(z)): 0.0171 / 0.0194 [986/1000][0/4] Loss_D: 0.1028 Loss_G: 5.3189 D(x): 0.9903 D(G(z)): 0.0847 / 0.0079 [987/1000][0/4] Loss_D: 0.0778 Loss_G: 5.3133 D(x): 0.9834 D(G(z)): 0.0565 / 0.0093 [988/1000][0/4] Loss_D: 0.0541 Loss_G: 4.7271 D(x): 0.9789 D(G(z)): 0.0314 / 0.0147 [989/1000][0/4] Loss_D: 0.0773 Loss_G: 5.1547 D(x): 0.9910 D(G(z)): 0.0639 / 0.0091 [990/1000][0/4] Loss_D: 0.0479 Loss_G: 4.8163 D(x): 0.9729 D(G(z)): 0.0195 / 0.0135 [991/1000][0/4] Loss_D: 0.0577 Loss_G: 4.2146 D(x): 0.9669 D(G(z)): 0.0225 / 0.0232 [992/1000][0/4] Loss_D: 0.0475 Loss_G: 4.6179 D(x): 0.9789 D(G(z)): 0.0252 / 0.0159 [993/1000][0/4] Loss_D: 0.0748 Loss_G: 5.3377 D(x): 0.9897 D(G(z)): 0.0596 / 0.0080 [994/1000][0/4] Loss_D: 0.0519 Loss_G: 4.6518 D(x): 0.9684 D(G(z)): 0.0188 / 0.0164 [995/1000][0/4] Loss_D: 0.0544 Loss_G: 4.2822 D(x): 0.9689 D(G(z)): 0.0220 / 0.0240 [996/1000][0/4] Loss_D: 0.0515 Loss_G: 4.6906 D(x): 0.9838 D(G(z)): 0.0339 / 0.0140 [997/1000][0/4] Loss_D: 0.0679 Loss_G: 4.1732 D(x): 0.9435 D(G(z)): 0.0078 / 0.0293 [998/1000][0/4] Loss_D: 0.0539 Loss_G: 4.1509 D(x): 0.9692 D(G(z)): 0.0211 / 0.0264 [999/1000][0/4] Loss_D: 0.0405 Loss_G: 4.7926 D(x): 0.9784 D(G(z)): 0.0182 / 0.0128
plt.figure(figsize=(10,5))
plt.title("Generator and Discriminator Loss During Training")
plt.plot(G_losses,label="G")
plt.plot(D_losses,label="D")
plt.xlabel("iterations")
plt.ylabel("Loss")
plt.legend()
plt.show()
fig = plt.figure(figsize=(8,8))
plt.axis("off")
ims = [[plt.imshow(np.transpose(i,(1,2,0)), animated=True)] for i in img_list]
ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True)
HTML(ani.to_jshtml())